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Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818)
* Python: Provider-leading client design & OpenAI package extraction Major refactoring of the Python Agent Framework client architecture: - Extract OpenAI clients into new `agent-framework-openai` package - Core package no longer depends on openai, azure-identity, azure-ai-projects - Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient, OpenAIChatClient → OpenAIChatCompletionClient - Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param - New FoundryChatClient for Azure AI Foundry Responses API - New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents - Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO - Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient - Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/ - ADR-0020: Provider-Leading Client Design Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: missing Agent imports in samples, .model_id → .model in foundry_local sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: CI failures — mypy errors, coverage targets, sample imports - azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref - Coverage: replace core.azure/openai targets with openai package target - project_provider: add type annotation for opts dict Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: populate openai .pyi stub, fix broken README links, coverage targets Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixes * updated observabilitty * reset azure init.pyi * fix errors * updated adr number * fix foundry local * fixed not renamed docstrings and comments, and added deprecated markers to old classes * fix tests and pyprojects * fix test vars * updated function tests * update durable * updated test setup for functions * Fix Foundry auth in workflow samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Stabilize Python integration workflows Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Update hosting samples for Foundry Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger full CI rerun Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger CI rerun again Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * trigger rerun * trigger rerun * fix for litellm * undo durabletask changes * Move Foundry APIs into foundry namespace Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix Foundry pyproject formatting Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Split provider samples by Foundry surface Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Restore hosting sample requirements Also fix the Foundry Local sample link after the provider sample move. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated tests * udpated foundry integration tests * removed dist from azurefunctions tests * Use separate Foundry clients for concurrent agents Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix client setup in azfunc and durable * disabled two tests * updated setup for some function and durable tests * improved azure openai setup with new clients * ignore deprecated * fixes * skip 11 * remove openai assistants int tests --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
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GitHub
Unverified
GitHub
parent
4b533608b6
commit
5e056b672e
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# Copyright (c) Microsoft. All rights reserved.
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import importlib.metadata
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from ._foundry_agent import FoundryAgent, RawFoundryAgent
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from ._foundry_agent_client import RawFoundryAgentChatClient
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from ._foundry_chat_client import FoundryChatClient, FoundryChatOptions, RawFoundryChatClient
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from ._foundry_memory_provider import FoundryMemoryProvider
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try:
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__version__ = importlib.metadata.version(__name__)
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except importlib.metadata.PackageNotFoundError:
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__version__ = "0.0.0"
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__all__ = [
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"FoundryAgent",
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"FoundryChatClient",
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"FoundryChatOptions",
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"FoundryMemoryProvider",
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"RawFoundryAgent",
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"RawFoundryAgentChatClient",
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"RawFoundryChatClient",
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"__version__",
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]
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@@ -0,0 +1,67 @@
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# Copyright (c) Microsoft. All rights reserved.
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from __future__ import annotations
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import logging
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from collections.abc import Awaitable, Callable
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from typing import Union
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from agent_framework.exceptions import ChatClientInvalidAuthException
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from azure.core.credentials import TokenCredential
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from azure.core.credentials_async import AsyncTokenCredential
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logger: logging.Logger = logging.getLogger(__name__)
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AzureTokenProvider = Callable[[], Union[str, Awaitable[str]]]
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"""A callable that returns a bearer token string, either synchronously or asynchronously."""
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AzureCredentialTypes = Union[TokenCredential, AsyncTokenCredential]
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"""Union of Azure credential types.
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Accepts:
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- ``TokenCredential`` — synchronous Azure credential (e.g. ``DefaultAzureCredential()``)
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- ``AsyncTokenCredential`` — asynchronous Azure credential (e.g. ``azure.identity.aio.DefaultAzureCredential()``)
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"""
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def resolve_credential_to_token_provider(
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credential: AzureCredentialTypes | AzureTokenProvider,
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token_endpoint: str | None,
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) -> AzureTokenProvider:
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"""Convert an Azure credential or token provider into an ``ad_token_provider`` callable.
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If the credential is already a callable token provider, it is returned as-is
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(``token_endpoint`` is not required in this case).
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If it is a ``TokenCredential`` or ``AsyncTokenCredential``, it is wrapped using
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``azure.identity.get_bearer_token_provider`` (sync or async variant) which
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handles token caching and automatic refresh.
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Args:
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credential: An Azure credential or token provider callable.
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token_endpoint: The token scope/endpoint
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(e.g. ``"https://cognitiveservices.azure.com/.default"``).
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Required when ``credential`` is a ``TokenCredential`` or ``AsyncTokenCredential``.
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Returns:
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A callable that returns a bearer token string (sync or async).
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Raises:
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ServiceInvalidAuthError: If the token endpoint is empty when needed for credential wrapping.
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"""
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# Already a token provider callable (not a credential object) — use directly
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if callable(credential) and not isinstance(credential, (TokenCredential, AsyncTokenCredential)):
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return credential
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if not token_endpoint:
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raise ChatClientInvalidAuthException(
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"A token endpoint must be provided either in settings, as an environment variable, or as an argument."
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)
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if isinstance(credential, AsyncTokenCredential):
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from azure.identity.aio import get_bearer_token_provider as get_async_bearer_token_provider
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return get_async_bearer_token_provider(credential, token_endpoint)
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from azure.identity import get_bearer_token_provider
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return get_bearer_token_provider(credential, token_endpoint) # type: ignore[arg-type]
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@@ -0,0 +1,287 @@
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# Copyright (c) Microsoft. All rights reserved.
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"""Microsoft Foundry Agent for connecting to pre-configured agents in Foundry.
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This module provides ``RawFoundryAgent`` and ``FoundryAgent`` — Agent subclasses
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that connect to existing PromptAgents or HostedAgents in Foundry. Use
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``FoundryAgent`` for the recommended experience with full middleware and telemetry.
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"""
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from __future__ import annotations
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import logging
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import sys
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from collections.abc import Callable, Sequence
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from typing import TYPE_CHECKING, Any
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from agent_framework import (
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AgentMiddlewareLayer,
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BaseContextProvider,
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RawAgent,
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)
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from agent_framework.observability import AgentTelemetryLayer
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from azure.ai.projects.aio import AIProjectClient
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from ._entra_id_authentication import AzureCredentialTypes
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from ._foundry_agent_client import (
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RawFoundryAgentChatClient,
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_FoundryAgentChatClient, # pyright: ignore[reportPrivateUsage]
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)
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if sys.version_info >= (3, 13):
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from typing import TypeVar # type: ignore # pragma: no cover
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else:
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from typing_extensions import TypeVar # type: ignore # pragma: no cover
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if sys.version_info >= (3, 11):
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from typing import TypedDict # type: ignore # pragma: no cover
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else:
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from typing_extensions import TypedDict # type: ignore # pragma: no cover
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if TYPE_CHECKING:
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from agent_framework._middleware import MiddlewareTypes
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from agent_framework._tools import FunctionTool
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from agent_framework_openai._chat_client import OpenAIChatOptions
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logger: logging.Logger = logging.getLogger("agent_framework.foundry")
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FoundryAgentOptionsT = TypeVar(
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"FoundryAgentOptionsT",
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bound=TypedDict, # type: ignore[valid-type]
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default="OpenAIChatOptions",
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covariant=True,
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)
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class RawFoundryAgent( # type: ignore[misc]
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RawAgent[FoundryAgentOptionsT],
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):
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"""Raw Microsoft Foundry Agent without agent-level middleware or telemetry.
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Connects to an existing PromptAgent or HostedAgent in Foundry.
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For full middleware and telemetry support, use :class:`FoundryAgent`.
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Examples:
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.. code-block:: python
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from agent_framework.foundry import RawFoundryAgent
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from azure.identity import AzureCliCredential
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agent = RawFoundryAgent(
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project_endpoint="https://your-project.services.ai.azure.com",
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agent_name="my-prompt-agent",
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agent_version="1.0",
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credential=AzureCliCredential(),
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)
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result = await agent.run("Hello!")
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"""
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def __init__(
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self,
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*,
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project_endpoint: str | None = None,
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agent_name: str | None = None,
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agent_version: str | None = None,
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credential: AzureCredentialTypes | None = None,
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project_client: AIProjectClient | None = None,
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allow_preview: bool | None = None,
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tools: FunctionTool | Callable[..., Any] | Sequence[FunctionTool | Callable[..., Any]] | None = None,
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context_providers: Sequence[BaseContextProvider] | None = None,
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client_type: type[RawFoundryAgentChatClient] | None = None,
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env_file_path: str | None = None,
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env_file_encoding: str | None = None,
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**kwargs: Any,
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) -> None:
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"""Initialize a Foundry Agent.
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Keyword Args:
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project_endpoint: The Foundry project endpoint URL.
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Can also be set via environment variable FOUNDRY_PROJECT_ENDPOINT.
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agent_name: The name of the Foundry agent to connect to.
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Can also be set via environment variable FOUNDRY_AGENT_NAME.
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agent_version: The version of the agent (required for PromptAgents, optional for HostedAgents).
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Can also be set via environment variable FOUNDRY_AGENT_VERSION.
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credential: Azure credential for authentication.
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project_client: An existing AIProjectClient to use.
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allow_preview: Enables preview opt-in on internally-created AIProjectClient.
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tools: Function tools to provide to the agent. Only ``FunctionTool`` objects are accepted.
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context_providers: Optional context providers for injecting dynamic context.
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client_type: Custom client class to use (must be a subclass of ``RawFoundryAgentChatClient``).
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Defaults to ``_FoundryAgentChatClient`` (full client middleware).
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env_file_path: Path to .env file for settings.
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env_file_encoding: Encoding for .env file.
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kwargs: Additional keyword arguments passed to the Agent base class.
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"""
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# Create the client
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actual_client_type = client_type or _FoundryAgentChatClient
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if not issubclass(actual_client_type, RawFoundryAgentChatClient):
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raise TypeError(
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f"client_type must be a subclass of RawFoundryAgentChatClient, got {actual_client_type.__name__}"
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)
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client = actual_client_type(
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project_endpoint=project_endpoint,
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agent_name=agent_name,
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agent_version=agent_version,
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credential=credential,
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project_client=project_client,
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allow_preview=allow_preview,
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env_file_path=env_file_path,
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env_file_encoding=env_file_encoding,
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)
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super().__init__(
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client=client, # type: ignore[arg-type]
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tools=tools, # type: ignore[arg-type]
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context_providers=context_providers,
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**kwargs,
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)
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async def configure_azure_monitor(
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self,
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enable_sensitive_data: bool = False,
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**kwargs: Any,
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) -> None:
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"""Setup observability with Azure Monitor (Microsoft Foundry integration).
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This method configures Azure Monitor for telemetry collection using the
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connection string from the Foundry project client (accessed via the internal client).
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Args:
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enable_sensitive_data: Enable sensitive data logging (prompts, responses).
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Should only be enabled in development/test environments. Default is False.
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**kwargs: Additional arguments passed to configure_azure_monitor().
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Raises:
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ImportError: If azure-monitor-opentelemetry-exporter is not installed.
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"""
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from azure.core.exceptions import ResourceNotFoundError
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from ._foundry_agent_client import RawFoundryAgentChatClient
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client = self.client
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if not isinstance(client, RawFoundryAgentChatClient):
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raise TypeError("configure_azure_monitor requires a RawFoundryAgentChatClient-based client.")
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try:
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conn_string = await client.project_client.telemetry.get_application_insights_connection_string()
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except ResourceNotFoundError:
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logger.warning(
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"No Application Insights connection string found for the Foundry project. "
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"Please ensure Application Insights is configured in your project, "
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"or call configure_otel_providers() manually with custom exporters."
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)
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return
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try:
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from azure.monitor.opentelemetry import configure_azure_monitor # type: ignore[import]
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except ImportError as exc:
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raise ImportError(
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"azure-monitor-opentelemetry is required for Azure Monitor integration. "
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"Install it with: pip install azure-monitor-opentelemetry"
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) from exc
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from agent_framework.observability import create_metric_views, create_resource, enable_instrumentation
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if "resource" not in kwargs:
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kwargs["resource"] = create_resource()
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configure_azure_monitor(
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connection_string=conn_string,
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views=create_metric_views(),
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**kwargs,
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)
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enable_instrumentation(enable_sensitive_data=enable_sensitive_data)
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class FoundryAgent( # type: ignore[misc]
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AgentTelemetryLayer,
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AgentMiddlewareLayer,
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RawFoundryAgent[FoundryAgentOptionsT],
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):
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"""Microsoft Foundry Agent with full middleware and telemetry support.
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Connects to an existing PromptAgent or HostedAgent in Foundry.
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This is the recommended class for production use.
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Examples:
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.. code-block:: python
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from agent_framework.foundry import FoundryAgent
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from azure.identity import AzureCliCredential
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# Connect to a PromptAgent
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agent = FoundryAgent(
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project_endpoint="https://your-project.services.ai.azure.com",
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agent_name="my-prompt-agent",
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agent_version="1.0",
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credential=AzureCliCredential(),
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tools=[my_function_tool],
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)
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result = await agent.run("Hello!")
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# Connect to a HostedAgent (no version needed)
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agent = FoundryAgent(
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project_endpoint="https://your-project.services.ai.azure.com",
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agent_name="my-hosted-agent",
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credential=AzureCliCredential(),
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)
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# Custom client (e.g., raw client without client middleware)
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agent = FoundryAgent(
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project_endpoint="https://your-project.services.ai.azure.com",
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agent_name="my-agent",
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credential=AzureCliCredential(),
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client_type=RawFoundryAgentChatClient,
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)
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"""
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def __init__(
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self,
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*,
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project_endpoint: str | None = None,
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agent_name: str | None = None,
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agent_version: str | None = None,
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credential: AzureCredentialTypes | None = None,
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project_client: AIProjectClient | None = None,
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allow_preview: bool | None = None,
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tools: FunctionTool | Callable[..., Any] | Sequence[FunctionTool | Callable[..., Any]] | None = None,
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context_providers: Sequence[BaseContextProvider] | None = None,
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middleware: Sequence[MiddlewareTypes] | None = None,
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client_type: type[RawFoundryAgentChatClient] | None = None,
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env_file_path: str | None = None,
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env_file_encoding: str | None = None,
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**kwargs: Any,
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) -> None:
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"""Initialize a Foundry Agent with full middleware and telemetry.
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Keyword Args:
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project_endpoint: The Foundry project endpoint URL.
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agent_name: The name of the Foundry agent to connect to.
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agent_version: The version of the agent (for PromptAgents).
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credential: Azure credential for authentication.
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project_client: An existing AIProjectClient to use.
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allow_preview: Enables preview opt-in on internally-created AIProjectClient.
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tools: Function tools to provide to the agent. Only ``FunctionTool`` objects are accepted.
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context_providers: Optional context providers.
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middleware: Optional agent-level middleware.
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client_type: Custom client class (must subclass ``RawFoundryAgentChatClient``).
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env_file_path: Path to .env file for settings.
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env_file_encoding: Encoding for .env file.
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kwargs: Additional keyword arguments.
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"""
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super().__init__(
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project_endpoint=project_endpoint,
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agent_name=agent_name,
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agent_version=agent_version,
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credential=credential,
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project_client=project_client,
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allow_preview=allow_preview,
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tools=tools,
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context_providers=context_providers,
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middleware=middleware,
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client_type=client_type,
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env_file_path=env_file_path,
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env_file_encoding=env_file_encoding,
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**kwargs,
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)
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@@ -0,0 +1,395 @@
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# Copyright (c) Microsoft. All rights reserved.
|
||||
|
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"""Microsoft Foundry Agent client for connecting to pre-configured agents in Foundry.
|
||||
|
||||
This module provides ``RawFoundryAgentClient`` and ``FoundryAgentClient`` for
|
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communicating with PromptAgents and HostedAgents via the Responses API.
|
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"""
|
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|
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from __future__ import annotations
|
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|
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import logging
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import sys
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from collections.abc import Callable, Mapping, MutableMapping, Sequence
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from typing import TYPE_CHECKING, Any, ClassVar, Generic, cast
|
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|
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from agent_framework._middleware import ChatMiddlewareLayer
|
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from agent_framework._settings import load_settings
|
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from agent_framework._telemetry import AGENT_FRAMEWORK_USER_AGENT
|
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from agent_framework._tools import FunctionInvocationConfiguration, FunctionInvocationLayer, FunctionTool
|
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from agent_framework._types import Message
|
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from agent_framework.observability import ChatTelemetryLayer
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from agent_framework_openai._chat_client import OpenAIChatOptions, RawOpenAIChatClient
|
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from azure.ai.projects.aio import AIProjectClient
|
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|
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from ._entra_id_authentication import AzureCredentialTypes
|
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|
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logger: logging.Logger = logging.getLogger(__name__)
|
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|
||||
if sys.version_info >= (3, 13):
|
||||
from typing import TypeVar # type: ignore # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import TypeVar # type: ignore # pragma: no cover
|
||||
if sys.version_info >= (3, 12):
|
||||
from typing import override # type: ignore # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import override # type: ignore # pragma: no cover
|
||||
if sys.version_info >= (3, 11):
|
||||
from typing import TypedDict # type: ignore # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import TypedDict # type: ignore # pragma: no cover
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from agent_framework import Agent, BaseContextProvider
|
||||
from agent_framework._middleware import (
|
||||
ChatMiddleware,
|
||||
ChatMiddlewareCallable,
|
||||
FunctionMiddleware,
|
||||
FunctionMiddlewareCallable,
|
||||
MiddlewareTypes,
|
||||
)
|
||||
from agent_framework._tools import ToolTypes
|
||||
|
||||
|
||||
class FoundryAgentSettings(TypedDict, total=False):
|
||||
"""Settings for Microsoft FoundryAgentClient resolved from args and environment.
|
||||
|
||||
Keyword Args:
|
||||
project_endpoint: The Foundry project endpoint URL.
|
||||
Can be set via environment variable FOUNDRY_PROJECT_ENDPOINT.
|
||||
agent_name: The name of the Foundry agent to connect to.
|
||||
Can be set via environment variable FOUNDRY_AGENT_NAME.
|
||||
agent_version: The version of the Foundry agent (for PromptAgents).
|
||||
Can be set via environment variable FOUNDRY_AGENT_VERSION.
|
||||
"""
|
||||
|
||||
project_endpoint: str | None
|
||||
agent_name: str | None
|
||||
agent_version: str | None
|
||||
|
||||
|
||||
FoundryAgentOptionsT = TypeVar(
|
||||
"FoundryAgentOptionsT",
|
||||
bound=TypedDict, # type: ignore[valid-type]
|
||||
default="OpenAIChatOptions",
|
||||
covariant=True,
|
||||
)
|
||||
|
||||
|
||||
class RawFoundryAgentChatClient( # type: ignore[misc]
|
||||
RawOpenAIChatClient[FoundryAgentOptionsT],
|
||||
Generic[FoundryAgentOptionsT],
|
||||
):
|
||||
"""Raw Microsoft Foundry Agent chat client for connecting to pre-configured agents in Foundry.
|
||||
|
||||
Connects to existing PromptAgents or HostedAgents via the Responses API.
|
||||
Does not create or delete agents — the agent must already exist in Foundry.
|
||||
|
||||
This is a raw client without function invocation, chat middleware, or telemetry layers.
|
||||
Tools passed in options are validated (only ``FunctionTool`` allowed) but **not invoked** —
|
||||
the function invocation loop is handled by ``_FoundryAgentChatClient`` or a custom subclass
|
||||
that includes ``FunctionInvocationLayer``.
|
||||
|
||||
Use this class as an extension point when building a custom client with specific middleware
|
||||
layers via subclassing::
|
||||
|
||||
from agent_framework._tools import FunctionInvocationLayer
|
||||
from agent_framework.foundry import RawFoundryAgentChatClient
|
||||
|
||||
|
||||
class MyClient(FunctionInvocationLayer, RawFoundryAgentChatClient):
|
||||
pass
|
||||
|
||||
|
||||
agent = FoundryAgent(..., client_type=MyClient)
|
||||
"""
|
||||
|
||||
OTEL_PROVIDER_NAME: ClassVar[str] = "azure.ai.foundry"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
project_endpoint: str | None = None,
|
||||
agent_name: str | None = None,
|
||||
agent_version: str | None = None,
|
||||
credential: AzureCredentialTypes | None = None,
|
||||
project_client: AIProjectClient | None = None,
|
||||
allow_preview: bool | None = None,
|
||||
env_file_path: str | None = None,
|
||||
env_file_encoding: str | None = None,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Initialize a raw Foundry Agent client.
|
||||
|
||||
Keyword Args:
|
||||
project_endpoint: The Foundry project endpoint URL.
|
||||
Can also be set via environment variable FOUNDRY_PROJECT_ENDPOINT.
|
||||
agent_name: The name of the Foundry agent to connect to.
|
||||
Can also be set via environment variable FOUNDRY_AGENT_NAME.
|
||||
agent_version: The version of the agent (required for PromptAgents, optional for HostedAgents).
|
||||
Can also be set via environment variable FOUNDRY_AGENT_VERSION.
|
||||
credential: Azure credential for authentication.
|
||||
project_client: An existing AIProjectClient to use.
|
||||
allow_preview: Enables preview opt-in on internally-created AIProjectClient.
|
||||
env_file_path: Path to .env file for settings.
|
||||
env_file_encoding: Encoding for .env file.
|
||||
kwargs: Additional keyword arguments.
|
||||
"""
|
||||
settings = load_settings(
|
||||
FoundryAgentSettings,
|
||||
env_prefix="FOUNDRY_",
|
||||
project_endpoint=project_endpoint,
|
||||
agent_name=agent_name,
|
||||
agent_version=agent_version,
|
||||
env_file_path=env_file_path,
|
||||
env_file_encoding=env_file_encoding,
|
||||
)
|
||||
|
||||
resolved_endpoint = settings.get("project_endpoint")
|
||||
self.agent_name = settings.get("agent_name")
|
||||
self.agent_version = settings.get("agent_version")
|
||||
|
||||
if not self.agent_name:
|
||||
raise ValueError(
|
||||
"Agent name is required. Set via 'agent_name' parameter or 'FOUNDRY_AGENT_NAME' environment variable."
|
||||
)
|
||||
|
||||
# Create or use provided project client
|
||||
self._should_close_client = False
|
||||
if project_client is not None:
|
||||
self.project_client = project_client
|
||||
else:
|
||||
if not resolved_endpoint:
|
||||
raise ValueError(
|
||||
"Either 'project_endpoint' or 'project_client' is required. "
|
||||
"Set project_endpoint via parameter or 'FOUNDRY_PROJECT_ENDPOINT' environment variable."
|
||||
)
|
||||
if not credential:
|
||||
raise ValueError("Azure credential is required when using project_endpoint without a project_client.")
|
||||
project_client_kwargs: dict[str, Any] = {
|
||||
"endpoint": resolved_endpoint,
|
||||
"credential": credential,
|
||||
"user_agent": AGENT_FRAMEWORK_USER_AGENT,
|
||||
}
|
||||
if allow_preview is not None:
|
||||
project_client_kwargs["allow_preview"] = allow_preview
|
||||
self.project_client = AIProjectClient(**project_client_kwargs)
|
||||
self._should_close_client = True
|
||||
|
||||
# Get OpenAI client from project
|
||||
async_client = self.project_client.get_openai_client()
|
||||
|
||||
super().__init__(async_client=async_client, **kwargs)
|
||||
|
||||
def _get_agent_reference(self) -> dict[str, str]:
|
||||
"""Build the agent reference dict for the Responses API."""
|
||||
ref: dict[str, str] = {"name": self.agent_name, "type": "agent_reference"} # type: ignore[dict-item]
|
||||
if self.agent_version:
|
||||
ref["version"] = self.agent_version
|
||||
return ref
|
||||
|
||||
@override
|
||||
def as_agent(
|
||||
self,
|
||||
*,
|
||||
id: str | None = None,
|
||||
name: str | None = None,
|
||||
description: str | None = None,
|
||||
instructions: str | None = None,
|
||||
tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
|
||||
default_options: FoundryAgentOptionsT | Mapping[str, Any] | None = None,
|
||||
context_providers: Sequence[BaseContextProvider] | None = None,
|
||||
middleware: Sequence[MiddlewareTypes] | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Agent[FoundryAgentOptionsT]:
|
||||
"""Create a FoundryAgent that reuses this client's Foundry configuration."""
|
||||
from ._foundry_agent import FoundryAgent
|
||||
|
||||
function_tools = cast(
|
||||
FunctionTool | Callable[..., Any] | Sequence[FunctionTool | Callable[..., Any]] | None,
|
||||
tools,
|
||||
)
|
||||
|
||||
return cast(
|
||||
"Agent[FoundryAgentOptionsT]",
|
||||
FoundryAgent(
|
||||
project_client=self.project_client,
|
||||
agent_name=self.agent_name,
|
||||
agent_version=self.agent_version,
|
||||
tools=function_tools,
|
||||
context_providers=context_providers,
|
||||
middleware=middleware,
|
||||
client_type=cast(type[RawFoundryAgentChatClient], self.__class__),
|
||||
id=id,
|
||||
name=self.agent_name if name is None else name,
|
||||
description=description,
|
||||
instructions=instructions,
|
||||
default_options=default_options,
|
||||
**kwargs,
|
||||
),
|
||||
)
|
||||
|
||||
@override
|
||||
async def _prepare_options(
|
||||
self,
|
||||
messages: Sequence[Message],
|
||||
options: Mapping[str, Any],
|
||||
**kwargs: Any,
|
||||
) -> dict[str, Any]:
|
||||
"""Prepare options for the Responses API, injecting agent reference and validating tools."""
|
||||
# Validate tools — only FunctionTool allowed
|
||||
tools = options.get("tools", [])
|
||||
if tools:
|
||||
for tool_item in tools:
|
||||
if not isinstance(tool_item, FunctionTool):
|
||||
raise TypeError(
|
||||
f"Only FunctionTool objects are accepted for Foundry agents, "
|
||||
f"got {type(tool_item).__name__}. Other tool types (MCPTool, dict schemas, "
|
||||
f"hosted tools) must be defined on the Foundry agent definition in the service."
|
||||
)
|
||||
|
||||
# Prepare messages: extract system/developer messages as instructions
|
||||
prepared_messages, _instructions = self._prepare_messages_for_azure_ai(messages)
|
||||
|
||||
# Call parent prepare_options (OpenAI Responses API format)
|
||||
run_options = await super()._prepare_options(prepared_messages, options, **kwargs)
|
||||
|
||||
# Apply Azure AI schema transforms
|
||||
if "input" in run_options and isinstance(run_options["input"], list):
|
||||
run_options["input"] = self._transform_input_for_azure_ai(cast(list[dict[str, Any]], run_options["input"]))
|
||||
|
||||
# Inject agent reference
|
||||
run_options["extra_body"] = {"agent_reference": self._get_agent_reference()}
|
||||
|
||||
return run_options
|
||||
|
||||
@override
|
||||
def _check_model_presence(self, options: dict[str, Any]) -> None:
|
||||
"""Skip model check — model is configured on the Foundry agent."""
|
||||
pass
|
||||
|
||||
def _prepare_messages_for_azure_ai(self, messages: Sequence[Message]) -> tuple[list[Message], str | None]:
|
||||
"""Extract system/developer messages as instructions for Azure AI.
|
||||
|
||||
Foundry agents may not support system/developer messages directly.
|
||||
Instead, extract them as instructions to prepend.
|
||||
"""
|
||||
prepared: list[Message] = []
|
||||
instructions_parts: list[str] = []
|
||||
for msg in messages:
|
||||
if msg.role in ("system", "developer"):
|
||||
if msg.text:
|
||||
instructions_parts.append(msg.text)
|
||||
else:
|
||||
prepared.append(msg)
|
||||
instructions = "\n".join(instructions_parts) if instructions_parts else None
|
||||
return prepared, instructions
|
||||
|
||||
def _transform_input_for_azure_ai(self, input_items: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
"""Transform input items to match Azure AI Projects expected schema.
|
||||
|
||||
Azure AI Projects 'create responses' API expects 'type' at item level
|
||||
and 'annotations' for output_text content items.
|
||||
"""
|
||||
transformed: list[dict[str, Any]] = []
|
||||
for item in input_items:
|
||||
new_item: dict[str, Any] = dict(item)
|
||||
|
||||
if "role" in new_item and "type" not in new_item:
|
||||
new_item["type"] = "message"
|
||||
|
||||
if (content := new_item.get("content")) and isinstance(content, list):
|
||||
new_content: list[Any] = []
|
||||
for content_item in content: # type: ignore[union-attr]
|
||||
if isinstance(content_item, MutableMapping):
|
||||
if content_item.get("type") == "output_text" and "annotations" not in content_item: # type: ignore[operator]
|
||||
content_item["annotations"] = []
|
||||
new_content.append(content_item)
|
||||
else:
|
||||
new_content.append(content_item)
|
||||
new_item["content"] = new_content
|
||||
|
||||
transformed.append(new_item)
|
||||
|
||||
return transformed
|
||||
|
||||
async def close(self) -> None:
|
||||
"""Close the project client if we created it."""
|
||||
if self._should_close_client:
|
||||
await self.project_client.close()
|
||||
|
||||
|
||||
class _FoundryAgentChatClient( # type: ignore[misc]
|
||||
FunctionInvocationLayer[FoundryAgentOptionsT],
|
||||
ChatMiddlewareLayer[FoundryAgentOptionsT],
|
||||
ChatTelemetryLayer[FoundryAgentOptionsT],
|
||||
RawFoundryAgentChatClient[FoundryAgentOptionsT],
|
||||
Generic[FoundryAgentOptionsT],
|
||||
):
|
||||
"""Microsoft Foundry Agent client with middleware, telemetry, and function invocation support.
|
||||
|
||||
Connects to existing PromptAgents or HostedAgents in Foundry.
|
||||
|
||||
Examples:
|
||||
.. code-block:: python
|
||||
|
||||
from agent_framework import Agent
|
||||
from agent_framework.foundry import FoundryAgentClient
|
||||
from azure.identity import AzureCliCredential
|
||||
|
||||
client = FoundryAgentClient(
|
||||
project_endpoint="https://your-project.services.ai.azure.com",
|
||||
agent_name="my-prompt-agent",
|
||||
agent_version="1.0",
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
|
||||
agent = Agent(client=client, tools=[my_function_tool])
|
||||
result = await agent.run("Hello!")
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
project_endpoint: str | None = None,
|
||||
agent_name: str | None = None,
|
||||
agent_version: str | None = None,
|
||||
credential: AzureCredentialTypes | None = None,
|
||||
project_client: AIProjectClient | None = None,
|
||||
allow_preview: bool | None = None,
|
||||
env_file_path: str | None = None,
|
||||
env_file_encoding: str | None = None,
|
||||
middleware: (
|
||||
Sequence[ChatMiddleware | ChatMiddlewareCallable | FunctionMiddleware | FunctionMiddlewareCallable] | None
|
||||
) = None,
|
||||
function_invocation_configuration: FunctionInvocationConfiguration | None = None,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Initialize a Foundry Agent client with full middleware support.
|
||||
|
||||
Keyword Args:
|
||||
project_endpoint: The Foundry project endpoint URL.
|
||||
agent_name: The name of the Foundry agent to connect to.
|
||||
agent_version: The version of the agent (for PromptAgents).
|
||||
credential: Azure credential for authentication.
|
||||
project_client: An existing AIProjectClient to use.
|
||||
allow_preview: Enables preview opt-in on internally-created AIProjectClient.
|
||||
env_file_path: Path to .env file for settings.
|
||||
env_file_encoding: Encoding for .env file.
|
||||
middleware: Optional sequence of middleware.
|
||||
function_invocation_configuration: Optional function invocation configuration.
|
||||
kwargs: Additional keyword arguments.
|
||||
"""
|
||||
super().__init__(
|
||||
project_endpoint=project_endpoint,
|
||||
agent_name=agent_name,
|
||||
agent_version=agent_version,
|
||||
credential=credential,
|
||||
project_client=project_client,
|
||||
allow_preview=allow_preview,
|
||||
env_file_path=env_file_path,
|
||||
env_file_encoding=env_file_encoding,
|
||||
middleware=middleware,
|
||||
function_invocation_configuration=function_invocation_configuration,
|
||||
**kwargs,
|
||||
)
|
||||
@@ -0,0 +1,530 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import sys
|
||||
from collections.abc import Sequence
|
||||
from typing import TYPE_CHECKING, Any, ClassVar, Generic, Literal
|
||||
|
||||
from agent_framework._middleware import ChatMiddlewareLayer
|
||||
from agent_framework._settings import load_settings
|
||||
from agent_framework._telemetry import AGENT_FRAMEWORK_USER_AGENT
|
||||
from agent_framework._tools import FunctionInvocationConfiguration, FunctionInvocationLayer
|
||||
from agent_framework._types import Content
|
||||
from agent_framework.observability import ChatTelemetryLayer
|
||||
from agent_framework_openai._chat_client import OpenAIChatOptions, RawOpenAIChatClient
|
||||
from azure.ai.projects.aio import AIProjectClient
|
||||
from azure.ai.projects.models import (
|
||||
AutoCodeInterpreterToolParam,
|
||||
CodeInterpreterTool,
|
||||
ImageGenTool,
|
||||
WebSearchApproximateLocation,
|
||||
WebSearchTool,
|
||||
WebSearchToolFilters,
|
||||
)
|
||||
from azure.ai.projects.models import FileSearchTool as ProjectsFileSearchTool
|
||||
from azure.ai.projects.models import MCPTool as FoundryMCPTool
|
||||
|
||||
from ._entra_id_authentication import AzureCredentialTypes, AzureTokenProvider
|
||||
from ._shared import resolve_file_ids
|
||||
|
||||
if sys.version_info >= (3, 13):
|
||||
from typing import TypeVar # type: ignore # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import TypeVar # type: ignore # pragma: no cover
|
||||
if sys.version_info >= (3, 12):
|
||||
from typing import override # type: ignore # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import override # type: ignore # pragma: no cover
|
||||
if sys.version_info >= (3, 11):
|
||||
from typing import TypedDict # type: ignore # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import TypedDict # type: ignore # pragma: no cover
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from agent_framework._middleware import (
|
||||
ChatMiddleware,
|
||||
ChatMiddlewareCallable,
|
||||
FunctionMiddleware,
|
||||
FunctionMiddlewareCallable,
|
||||
)
|
||||
|
||||
logger: logging.Logger = logging.getLogger("agent_framework.foundry")
|
||||
|
||||
|
||||
class FoundrySettings(TypedDict, total=False):
|
||||
"""Settings for Microsoft FoundryChatClient resolved from args and environment.
|
||||
|
||||
Keyword Args:
|
||||
model: The model deployment name.
|
||||
Can be set via environment variable FOUNDRY_MODEL.
|
||||
project_endpoint: The Microsoft Foundry project endpoint URL.
|
||||
Can be set via environment variable FOUNDRY_PROJECT_ENDPOINT.
|
||||
"""
|
||||
|
||||
model: str | None
|
||||
project_endpoint: str | None
|
||||
|
||||
|
||||
FoundryChatOptionsT = TypeVar(
|
||||
"FoundryChatOptionsT",
|
||||
bound=TypedDict, # type: ignore[valid-type]
|
||||
default="OpenAIChatOptions",
|
||||
covariant=True,
|
||||
)
|
||||
|
||||
FoundryChatOptions = OpenAIChatOptions
|
||||
|
||||
|
||||
class RawFoundryChatClient( # type: ignore[misc]
|
||||
RawOpenAIChatClient[FoundryChatOptionsT],
|
||||
Generic[FoundryChatOptionsT],
|
||||
):
|
||||
"""Raw Microsoft Foundry chat client using the OpenAI Responses API via a Foundry project.
|
||||
|
||||
This client creates an OpenAI-compatible client from a Foundry project
|
||||
and delegates to ``RawOpenAIChatClient`` for request handling.
|
||||
|
||||
Environment variables:
|
||||
- ``FOUNDRY_PROJECT_ENDPOINT`` to provide the Foundry project endpoint.
|
||||
- ``FOUNDRY_MODEL`` to provide the Foundry model deployment name.
|
||||
|
||||
Warning:
|
||||
**This class should not normally be used directly.** Use ``FoundryChatClient``
|
||||
for a fully-featured client with middleware, telemetry, and function invocation.
|
||||
"""
|
||||
|
||||
OTEL_PROVIDER_NAME: ClassVar[str] = "azure.ai.foundry" # type: ignore[reportIncompatibleVariableOverride, misc]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
project_endpoint: str | None = None,
|
||||
project_client: AIProjectClient | None = None,
|
||||
model: str | None = None,
|
||||
credential: AzureCredentialTypes | AzureTokenProvider | None = None,
|
||||
allow_preview: bool | None = None,
|
||||
env_file_path: str | None = None,
|
||||
env_file_encoding: str | None = None,
|
||||
instruction_role: str | None = None,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Initialize a raw Microsoft Foundry chat client.
|
||||
|
||||
Keyword Args:
|
||||
project_endpoint: The Foundry project endpoint URL.
|
||||
Can also be set via environment variable FOUNDRY_PROJECT_ENDPOINT.
|
||||
project_client: An existing AIProjectClient to use. If provided,
|
||||
the OpenAI client will be obtained via ``project_client.get_openai_client()``.
|
||||
model: The model deployment name.
|
||||
Can also be set via environment variable FOUNDRY_MODEL.
|
||||
credential: Azure credential or token provider for authentication.
|
||||
Required when using ``project_endpoint`` without a ``project_client``.
|
||||
allow_preview: Enables preview opt-in on internally-created AIProjectClient.
|
||||
env_file_path: Path to .env file for settings.
|
||||
env_file_encoding: Encoding for .env file.
|
||||
instruction_role: The role to use for 'instruction' messages.
|
||||
kwargs: Additional keyword arguments.
|
||||
"""
|
||||
foundry_settings = load_settings(
|
||||
FoundrySettings,
|
||||
env_prefix="FOUNDRY_",
|
||||
model=model,
|
||||
project_endpoint=project_endpoint,
|
||||
env_file_path=env_file_path,
|
||||
env_file_encoding=env_file_encoding,
|
||||
)
|
||||
|
||||
resolved_model = foundry_settings.get("model")
|
||||
if not resolved_model:
|
||||
raise ValueError("Model is required. Set via 'model' parameter or 'FOUNDRY_MODEL' environment variable.")
|
||||
|
||||
project_endpoint = foundry_settings.get("project_endpoint")
|
||||
|
||||
if project_endpoint is None and project_client is None:
|
||||
raise ValueError(
|
||||
"Either 'project_endpoint' or 'project_client' is required. "
|
||||
"Set project_endpoint via parameter or 'FOUNDRY_PROJECT_ENDPOINT' environment variable."
|
||||
)
|
||||
if not project_client:
|
||||
if not project_endpoint:
|
||||
raise ValueError(
|
||||
"Azure AI project endpoint is required. Set via 'project_endpoint' parameter "
|
||||
"or 'FOUNDRY_PROJECT_ENDPOINT' environment variable,"
|
||||
"or pass in a AIProjectClient."
|
||||
)
|
||||
if not credential:
|
||||
raise ValueError("Azure credential is required when using project_endpoint without a project_client.")
|
||||
project_client_kwargs: dict[str, Any] = {
|
||||
"endpoint": project_endpoint,
|
||||
"credential": credential, # type: ignore[arg-type]
|
||||
"user_agent": AGENT_FRAMEWORK_USER_AGENT,
|
||||
}
|
||||
if allow_preview is not None:
|
||||
project_client_kwargs["allow_preview"] = allow_preview
|
||||
project_client = AIProjectClient(**project_client_kwargs)
|
||||
|
||||
super().__init__(
|
||||
model=resolved_model,
|
||||
async_client=project_client.get_openai_client(),
|
||||
instruction_role=instruction_role,
|
||||
**kwargs,
|
||||
)
|
||||
self.project_client = project_client
|
||||
|
||||
@override
|
||||
def _check_model_presence(self, options: dict[str, Any]) -> None:
|
||||
if not options.get("model"):
|
||||
if not self.model:
|
||||
raise ValueError("model must be a non-empty string")
|
||||
options["model"] = self.model
|
||||
|
||||
async def configure_azure_monitor(
|
||||
self,
|
||||
enable_sensitive_data: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Setup observability with Azure Monitor (Microsoft Foundry integration).
|
||||
|
||||
This method configures Azure Monitor for telemetry collection using the
|
||||
connection string from the Foundry project client.
|
||||
|
||||
Args:
|
||||
enable_sensitive_data: Enable sensitive data logging (prompts, responses).
|
||||
Should only be enabled in development/test environments. Default is False.
|
||||
**kwargs: Additional arguments passed to configure_azure_monitor().
|
||||
Common options include:
|
||||
- enable_live_metrics (bool): Enable Azure Monitor Live Metrics
|
||||
- credential (TokenCredential): Azure credential for Entra ID auth
|
||||
- resource (Resource): Custom OpenTelemetry resource
|
||||
|
||||
Raises:
|
||||
ImportError: If azure-monitor-opentelemetry-exporter is not installed.
|
||||
"""
|
||||
from azure.core.exceptions import ResourceNotFoundError
|
||||
|
||||
try:
|
||||
conn_string = await self.project_client.telemetry.get_application_insights_connection_string()
|
||||
except ResourceNotFoundError:
|
||||
logger.warning(
|
||||
"No Application Insights connection string found for the Foundry project. "
|
||||
"Please ensure Application Insights is configured in your project, "
|
||||
"or call configure_otel_providers() manually with custom exporters."
|
||||
)
|
||||
return
|
||||
|
||||
try:
|
||||
from azure.monitor.opentelemetry import configure_azure_monitor # type: ignore[import]
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"azure-monitor-opentelemetry is required for Azure Monitor integration. "
|
||||
"Install it with: pip install azure-monitor-opentelemetry"
|
||||
) from exc
|
||||
|
||||
from agent_framework.observability import create_metric_views, create_resource, enable_instrumentation
|
||||
|
||||
if "resource" not in kwargs:
|
||||
kwargs["resource"] = create_resource()
|
||||
|
||||
configure_azure_monitor(
|
||||
connection_string=conn_string,
|
||||
views=create_metric_views(),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
enable_instrumentation(enable_sensitive_data=enable_sensitive_data)
|
||||
|
||||
# region Tool factory methods (override OpenAI defaults with Foundry versions)
|
||||
|
||||
@staticmethod
|
||||
def get_code_interpreter_tool( # type: ignore[override]
|
||||
*,
|
||||
file_ids: list[str | Content] | None = None,
|
||||
container: Literal["auto"] | dict[str, Any] = "auto",
|
||||
**kwargs: Any,
|
||||
) -> CodeInterpreterTool:
|
||||
"""Create a code interpreter tool configuration for Foundry.
|
||||
|
||||
Keyword Args:
|
||||
file_ids: Optional list of file IDs or Content objects to make available.
|
||||
container: Container configuration. Use "auto" for automatic management.
|
||||
**kwargs: Additional arguments passed to the SDK CodeInterpreterTool constructor.
|
||||
|
||||
Returns:
|
||||
A CodeInterpreterTool ready to pass to an Agent.
|
||||
"""
|
||||
if file_ids is None and isinstance(container, dict):
|
||||
file_ids = container.get("file_ids")
|
||||
resolved = resolve_file_ids(file_ids)
|
||||
tool_container = AutoCodeInterpreterToolParam(file_ids=resolved)
|
||||
return CodeInterpreterTool(container=tool_container, **kwargs)
|
||||
|
||||
@staticmethod
|
||||
def get_file_search_tool(
|
||||
*,
|
||||
vector_store_ids: list[str],
|
||||
max_num_results: int | None = None,
|
||||
ranking_options: dict[str, Any] | None = None,
|
||||
filters: dict[str, Any] | None = None,
|
||||
**kwargs: Any,
|
||||
) -> ProjectsFileSearchTool:
|
||||
"""Create a file search tool configuration for Foundry.
|
||||
|
||||
Keyword Args:
|
||||
vector_store_ids: List of vector store IDs to search.
|
||||
max_num_results: Maximum number of results to return (1-50).
|
||||
ranking_options: Ranking options for search results.
|
||||
filters: A filter to apply (ComparisonFilter or CompoundFilter).
|
||||
**kwargs: Additional arguments passed to the SDK FileSearchTool constructor.
|
||||
|
||||
Returns:
|
||||
A FileSearchTool ready to pass to an Agent.
|
||||
"""
|
||||
if not vector_store_ids:
|
||||
raise ValueError("File search tool requires 'vector_store_ids' to be specified.")
|
||||
return ProjectsFileSearchTool(
|
||||
vector_store_ids=vector_store_ids,
|
||||
max_num_results=max_num_results,
|
||||
ranking_options=ranking_options, # type: ignore[arg-type]
|
||||
filters=filters, # type: ignore[arg-type]
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def get_web_search_tool( # type: ignore[override]
|
||||
*,
|
||||
user_location: dict[str, str] | None = None,
|
||||
search_context_size: Literal["low", "medium", "high"] | None = None,
|
||||
allowed_domains: list[str] | None = None,
|
||||
custom_search_configuration: dict[str, Any] | None = None,
|
||||
**kwargs: Any,
|
||||
) -> WebSearchTool:
|
||||
"""Create a web search tool configuration for Microsoft Foundry.
|
||||
|
||||
Keyword Args:
|
||||
user_location: Location context with keys like "city", "country", "region", "timezone".
|
||||
search_context_size: Amount of context from search results ("low", "medium", "high").
|
||||
allowed_domains: List of domains to restrict search results to.
|
||||
custom_search_configuration: Custom Bing search configuration.
|
||||
**kwargs: Additional arguments passed to the SDK WebSearchTool constructor.
|
||||
|
||||
Returns:
|
||||
A WebSearchTool ready to pass to an Agent.
|
||||
"""
|
||||
ws_kwargs: dict[str, Any] = {**kwargs}
|
||||
if search_context_size:
|
||||
ws_kwargs["search_context_size"] = search_context_size
|
||||
if allowed_domains:
|
||||
ws_kwargs["filters"] = WebSearchToolFilters(allowed_domains=allowed_domains)
|
||||
if custom_search_configuration:
|
||||
ws_kwargs["custom_search_configuration"] = custom_search_configuration
|
||||
ws_tool = WebSearchTool(**ws_kwargs)
|
||||
if user_location:
|
||||
ws_tool.user_location = WebSearchApproximateLocation(
|
||||
city=user_location.get("city"),
|
||||
country=user_location.get("country"),
|
||||
region=user_location.get("region"),
|
||||
timezone=user_location.get("timezone"),
|
||||
)
|
||||
return ws_tool
|
||||
|
||||
@staticmethod
|
||||
def get_image_generation_tool( # type: ignore[override]
|
||||
*,
|
||||
model: Literal["gpt-image-1"] | str | None = None,
|
||||
size: Literal["1024x1024", "1024x1536", "1536x1024", "auto"] | None = None,
|
||||
output_format: Literal["png", "webp", "jpeg"] | None = None,
|
||||
quality: Literal["low", "medium", "high", "auto"] | None = None,
|
||||
background: Literal["transparent", "opaque", "auto"] | None = None,
|
||||
partial_images: int | None = None,
|
||||
moderation: Literal["auto", "low"] | None = None,
|
||||
output_compression: int | None = None,
|
||||
**kwargs: Any,
|
||||
) -> ImageGenTool:
|
||||
"""Create an image generation tool configuration for Foundry.
|
||||
|
||||
Keyword Args:
|
||||
model: The model to use for image generation.
|
||||
size: Output image size.
|
||||
output_format: Output image format.
|
||||
quality: Output image quality.
|
||||
background: Background transparency setting.
|
||||
partial_images: Number of partial images to return during generation.
|
||||
moderation: Moderation level.
|
||||
output_compression: Compression level.
|
||||
**kwargs: Additional arguments passed to the SDK ImageGenTool constructor.
|
||||
|
||||
Returns:
|
||||
An ImageGenTool ready to pass to an Agent.
|
||||
"""
|
||||
return ImageGenTool( # type: ignore[misc]
|
||||
model=model, # type: ignore[arg-type]
|
||||
size=size,
|
||||
output_format=output_format,
|
||||
quality=quality,
|
||||
background=background,
|
||||
partial_images=partial_images,
|
||||
moderation=moderation,
|
||||
output_compression=output_compression,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def get_mcp_tool(
|
||||
*,
|
||||
name: str,
|
||||
url: str | None = None,
|
||||
description: str | None = None,
|
||||
approval_mode: Literal["always_require", "never_require"] | dict[str, list[str]] | None = None,
|
||||
allowed_tools: list[str] | None = None,
|
||||
headers: dict[str, str] | None = None,
|
||||
project_connection_id: str | None = None,
|
||||
**kwargs: Any,
|
||||
) -> FoundryMCPTool:
|
||||
"""Create a hosted MCP tool configuration for Foundry.
|
||||
|
||||
This configures an MCP server that runs remotely on Azure AI, not locally.
|
||||
|
||||
Keyword Args:
|
||||
name: A label/name for the MCP server.
|
||||
url: The URL of the MCP server. Required if project_connection_id is not provided.
|
||||
description: A description of what the MCP server provides.
|
||||
approval_mode: Tool approval mode ("always_require", "never_require", or dict).
|
||||
allowed_tools: List of allowed tool names from this MCP server.
|
||||
headers: HTTP headers to include in requests to the MCP server.
|
||||
project_connection_id: Foundry connection ID for managed MCP connections.
|
||||
**kwargs: Additional arguments passed to the SDK MCPTool constructor.
|
||||
|
||||
Returns:
|
||||
An MCPTool configuration ready to pass to an Agent.
|
||||
"""
|
||||
mcp = FoundryMCPTool(server_label=name.replace(" ", "_"), server_url=url or "", **kwargs)
|
||||
|
||||
if description:
|
||||
mcp["server_description"] = description
|
||||
if project_connection_id:
|
||||
mcp["project_connection_id"] = project_connection_id
|
||||
elif headers:
|
||||
mcp["headers"] = headers
|
||||
if allowed_tools:
|
||||
mcp["allowed_tools"] = allowed_tools
|
||||
if approval_mode:
|
||||
if isinstance(approval_mode, str):
|
||||
mcp["require_approval"] = "always" if approval_mode == "always_require" else "never"
|
||||
else:
|
||||
if always_require := approval_mode.get("always_require_approval"):
|
||||
mcp["require_approval"] = {"always": {"tool_names": always_require}}
|
||||
if never_require := approval_mode.get("never_require_approval"):
|
||||
mcp["require_approval"] = {"never": {"tool_names": never_require}}
|
||||
|
||||
return mcp
|
||||
|
||||
# endregion
|
||||
|
||||
|
||||
class FoundryChatClient( # type: ignore[misc]
|
||||
FunctionInvocationLayer[FoundryChatOptionsT],
|
||||
ChatMiddlewareLayer[FoundryChatOptionsT],
|
||||
ChatTelemetryLayer[FoundryChatOptionsT],
|
||||
RawFoundryChatClient[FoundryChatOptionsT],
|
||||
Generic[FoundryChatOptionsT],
|
||||
):
|
||||
"""Microsoft Foundry chat client using the OpenAI Responses API.
|
||||
|
||||
Creates an OpenAI-compatible client from a Foundry project
|
||||
with middleware, telemetry, and function invocation support.
|
||||
|
||||
Environment variables:
|
||||
- ``FOUNDRY_PROJECT_ENDPOINT`` to provide the Foundry project endpoint.
|
||||
- ``FOUNDRY_MODEL`` to provide the Foundry model deployment name.
|
||||
|
||||
Keyword Args:
|
||||
project_endpoint: The Foundry project endpoint URL.
|
||||
Can also be set via environment variable ``FOUNDRY_PROJECT_ENDPOINT``.
|
||||
project_client: An existing AIProjectClient to use.
|
||||
model: The model deployment name.
|
||||
Can also be set via environment variable ``FOUNDRY_MODEL``.
|
||||
model_id: Deprecated alias for ``model``.
|
||||
credential: Azure credential or token provider for authentication.
|
||||
allow_preview: Enables preview opt-in on internally-created AIProjectClient.
|
||||
env_file_path: Path to .env file for settings.
|
||||
env_file_encoding: Encoding for .env file.
|
||||
instruction_role: The role to use for 'instruction' messages.
|
||||
middleware: Optional sequence of middleware.
|
||||
function_invocation_configuration: Optional function invocation configuration.
|
||||
|
||||
Examples:
|
||||
.. code-block:: python
|
||||
|
||||
from azure.identity import AzureCliCredential
|
||||
from agent_framework_foundry import FoundryChatClient
|
||||
|
||||
client = FoundryChatClient(
|
||||
project_endpoint="https://your-project.services.ai.azure.com",
|
||||
model="gpt-4o",
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
|
||||
# Or using an existing AIProjectClient
|
||||
from azure.ai.projects.aio import AIProjectClient
|
||||
|
||||
project_client = AIProjectClient(
|
||||
endpoint="https://your-project.services.ai.azure.com",
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
client = FoundryChatClient(
|
||||
project_client=project_client,
|
||||
model="gpt-4o",
|
||||
)
|
||||
"""
|
||||
|
||||
OTEL_PROVIDER_NAME: ClassVar[str] = "azure.ai.foundry" # type: ignore[reportIncompatibleVariableOverride, misc]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
project_endpoint: str | None = None,
|
||||
project_client: AIProjectClient | None = None,
|
||||
model: str | None = None,
|
||||
credential: AzureCredentialTypes | AzureTokenProvider | None = None,
|
||||
allow_preview: bool | None = None,
|
||||
env_file_path: str | None = None,
|
||||
env_file_encoding: str | None = None,
|
||||
instruction_role: str | None = None,
|
||||
middleware: (
|
||||
Sequence[ChatMiddleware | ChatMiddlewareCallable | FunctionMiddleware | FunctionMiddlewareCallable] | None
|
||||
) = None,
|
||||
function_invocation_configuration: FunctionInvocationConfiguration | None = None,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Initialize a Foundry chat client.
|
||||
|
||||
Keyword Args:
|
||||
project_endpoint: The Foundry project endpoint URL.
|
||||
Can also be set via environment variable ``FOUNDRY_PROJECT_ENDPOINT``.
|
||||
project_client: An existing AIProjectClient to use.
|
||||
model: The model deployment name.
|
||||
Can also be set via environment variable ``FOUNDRY_MODEL``.
|
||||
credential: Azure credential or token provider for authentication.
|
||||
allow_preview: Enables preview opt-in on internally-created AIProjectClient.
|
||||
env_file_path: Path to .env file for settings.
|
||||
env_file_encoding: Encoding for .env file.
|
||||
instruction_role: The role to use for 'instruction' messages.
|
||||
middleware: Optional sequence of middleware.
|
||||
function_invocation_configuration: Optional function invocation configuration.
|
||||
kwargs: Additional keyword arguments.
|
||||
"""
|
||||
super().__init__(
|
||||
project_endpoint=project_endpoint,
|
||||
project_client=project_client,
|
||||
model=model,
|
||||
credential=credential,
|
||||
allow_preview=allow_preview,
|
||||
env_file_path=env_file_path,
|
||||
env_file_encoding=env_file_encoding,
|
||||
instruction_role=instruction_role,
|
||||
middleware=middleware,
|
||||
function_invocation_configuration=function_invocation_configuration,
|
||||
**kwargs,
|
||||
)
|
||||
@@ -0,0 +1,261 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
"""Foundry Memory Context Provider using BaseContextProvider.
|
||||
|
||||
This module provides ``FoundryMemoryProvider``, built on
|
||||
:class:`BaseContextProvider`.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import sys
|
||||
from contextlib import AbstractAsyncContextManager
|
||||
from typing import TYPE_CHECKING, Any, ClassVar
|
||||
|
||||
from agent_framework import AGENT_FRAMEWORK_USER_AGENT, Message
|
||||
from agent_framework._sessions import AgentSession, BaseContextProvider, SessionContext
|
||||
from agent_framework._settings import load_settings
|
||||
from azure.ai.projects.aio import AIProjectClient
|
||||
from openai.types.responses import ResponseInputItemParam
|
||||
|
||||
from ._entra_id_authentication import AzureCredentialTypes
|
||||
from ._shared import FoundryProjectSettings
|
||||
|
||||
if sys.version_info >= (3, 11):
|
||||
from typing import Self # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import Self # pragma: no cover
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from agent_framework._agents import SupportsAgentRun
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class FoundryMemoryProvider(BaseContextProvider):
|
||||
"""Foundry Memory context provider using the new BaseContextProvider hooks pattern.
|
||||
|
||||
Integrates Azure AI Foundry Memory Store for persistent semantic memory,
|
||||
searching and storing memories via the Azure AI Projects SDK.
|
||||
|
||||
Args:
|
||||
source_id: Unique identifier for this provider instance.
|
||||
project_client: Azure AI Project client for memory operations.
|
||||
memory_store_name: The name of the memory store to use.
|
||||
scope: The namespace that logically groups and isolates memories (e.g., user ID).
|
||||
context_prompt: The prompt to prepend to retrieved memories.
|
||||
update_delay: Timeout period before processing memory update in seconds.
|
||||
Defaults to 300 (5 minutes). Set to 0 to immediately trigger updates.
|
||||
"""
|
||||
|
||||
DEFAULT_SOURCE_ID: ClassVar[str] = "foundry_memory"
|
||||
DEFAULT_CONTEXT_PROMPT = "## Memories\nConsider the following memories when answering user questions:"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
source_id: str = DEFAULT_SOURCE_ID,
|
||||
*,
|
||||
project_client: AIProjectClient | None = None,
|
||||
project_endpoint: str | None = None,
|
||||
credential: AzureCredentialTypes | None = None,
|
||||
allow_preview: bool | None = None,
|
||||
memory_store_name: str,
|
||||
scope: str | None = None,
|
||||
context_prompt: str | None = None,
|
||||
update_delay: int = 300,
|
||||
env_file_path: str | None = None,
|
||||
env_file_encoding: str | None = None,
|
||||
) -> None:
|
||||
"""Initialize the Foundry Memory context provider.
|
||||
|
||||
Args:
|
||||
source_id: Unique identifier for this provider instance.
|
||||
project_client: Azure AI Project client for memory operations.
|
||||
project_endpoint: Foundry project endpoint URL. Used when project_client is not provided.
|
||||
credential: Azure credential for authentication. Accepts a TokenCredential,
|
||||
AsyncTokenCredential, or a callable token provider.
|
||||
Required when project_client is not provided.
|
||||
allow_preview: Enables preview opt-in on internally-created ``AIProjectClient``.
|
||||
memory_store_name: The name of the memory store to use.
|
||||
scope: The namespace that logically groups and isolates memories (e.g., user ID).
|
||||
If None, `session_id` will be used.
|
||||
context_prompt: The prompt to prepend to retrieved memories.
|
||||
update_delay: Timeout period before processing memory update in seconds.
|
||||
env_file_path: Path to environment file for loading settings.
|
||||
env_file_encoding: Encoding of the environment file.
|
||||
"""
|
||||
super().__init__(source_id)
|
||||
foundry_settings = load_settings(
|
||||
FoundryProjectSettings,
|
||||
env_prefix="FOUNDRY_",
|
||||
project_endpoint=project_endpoint,
|
||||
env_file_path=env_file_path,
|
||||
env_file_encoding=env_file_encoding,
|
||||
)
|
||||
|
||||
if project_client is None:
|
||||
resolved_endpoint = foundry_settings.get("project_endpoint")
|
||||
if not resolved_endpoint:
|
||||
raise ValueError(
|
||||
"Foundry project endpoint is required. Set via 'project_endpoint' parameter "
|
||||
"or 'FOUNDRY_PROJECT_ENDPOINT' environment variable."
|
||||
)
|
||||
if not credential:
|
||||
raise ValueError("Azure credential is required when project_client is not provided.")
|
||||
project_client_kwargs: dict[str, Any] = {
|
||||
"endpoint": resolved_endpoint,
|
||||
"credential": credential, # type: ignore[arg-type]
|
||||
"user_agent": AGENT_FRAMEWORK_USER_AGENT,
|
||||
}
|
||||
if allow_preview is not None:
|
||||
project_client_kwargs["allow_preview"] = allow_preview
|
||||
project_client = AIProjectClient(**project_client_kwargs)
|
||||
|
||||
if not memory_store_name:
|
||||
raise ValueError("memory_store_name is required")
|
||||
if not scope:
|
||||
raise ValueError("scope is required")
|
||||
|
||||
self.project_client = project_client
|
||||
self.memory_store_name = memory_store_name
|
||||
self.scope = scope
|
||||
self.context_prompt = context_prompt or self.DEFAULT_CONTEXT_PROMPT
|
||||
self.update_delay = update_delay
|
||||
|
||||
async def __aenter__(self) -> Self:
|
||||
"""Async context manager entry."""
|
||||
if self.project_client and isinstance(self.project_client, AbstractAsyncContextManager):
|
||||
await self.project_client.__aenter__()
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: Any) -> None:
|
||||
"""Async context manager exit."""
|
||||
if self.project_client and isinstance(self.project_client, AbstractAsyncContextManager):
|
||||
await self.project_client.__aexit__(exc_type, exc_val, exc_tb)
|
||||
|
||||
# -- Hooks pattern ---------------------------------------------------------
|
||||
|
||||
async def before_run(
|
||||
self,
|
||||
*,
|
||||
agent: SupportsAgentRun,
|
||||
session: AgentSession,
|
||||
context: SessionContext,
|
||||
state: dict[str, Any],
|
||||
) -> None:
|
||||
"""Search Foundry Memory for relevant memories and add to the session context.
|
||||
|
||||
This method:
|
||||
1. Retrieves static memories (user profile) on first call per session
|
||||
2. Searches for contextual memories based on input messages
|
||||
3. Combines and injects memories into the context
|
||||
"""
|
||||
# On first run, retrieve static memories (user profile memories)
|
||||
if not state.get("initialized"):
|
||||
try:
|
||||
static_search_result = await self.project_client.beta.memory_stores.search_memories(
|
||||
name=self.memory_store_name,
|
||||
scope=self.scope or context.session_id, # type: ignore[arg-type]
|
||||
)
|
||||
static_memories = [{"content": memory.memory_item.content} for memory in static_search_result.memories]
|
||||
state["static_memories"] = static_memories
|
||||
except Exception as e:
|
||||
# Log but don't fail - memory retrieval is non-critical
|
||||
logger.warning(f"Failed to retrieve static memories: {e}")
|
||||
state["static_memories"] = []
|
||||
finally:
|
||||
# Mark as initialized regardless of success to avoid repeated attempts
|
||||
state["initialized"] = True
|
||||
|
||||
# Search for contextual memories based on input messages
|
||||
# Check if there are any non-empty input messages
|
||||
has_input = any(msg and msg.text and msg.text.strip() for msg in context.input_messages)
|
||||
if not has_input:
|
||||
return
|
||||
|
||||
# Convert input messages to memory search item format
|
||||
items: list[ResponseInputItemParam] = [
|
||||
{"type": "message", "role": "user", "content": msg.text}
|
||||
for msg in context.input_messages
|
||||
if msg and msg.text and msg.text.strip()
|
||||
]
|
||||
|
||||
try:
|
||||
search_result = await self.project_client.beta.memory_stores.search_memories(
|
||||
name=self.memory_store_name,
|
||||
scope=self.scope or context.session_id, # type: ignore[arg-type]
|
||||
items=items,
|
||||
previous_search_id=state.get("previous_search_id"),
|
||||
)
|
||||
|
||||
# Extract search_id for next incremental search
|
||||
if search_result.memories:
|
||||
state["previous_search_id"] = search_result.search_id
|
||||
|
||||
# Combine static and contextual memories
|
||||
contextual_memories = [{"content": memory.memory_item.content} for memory in search_result.memories]
|
||||
|
||||
all_memories = state.get("static_memories", []) + contextual_memories
|
||||
|
||||
# Inject memories into context
|
||||
if all_memories:
|
||||
line_separated_memories = "\n".join(
|
||||
str(memory.get("content", "")) for memory in all_memories if memory.get("content")
|
||||
)
|
||||
if line_separated_memories:
|
||||
context.extend_messages(
|
||||
self.source_id,
|
||||
[Message(role="user", text=f"{self.context_prompt}\n{line_separated_memories}")],
|
||||
)
|
||||
except Exception as e:
|
||||
# Log but don't fail - memory retrieval is non-critical
|
||||
logger.warning(f"Failed to search contextual memories: {e}")
|
||||
|
||||
async def after_run(
|
||||
self,
|
||||
*,
|
||||
agent: SupportsAgentRun,
|
||||
session: AgentSession,
|
||||
context: SessionContext,
|
||||
state: dict[str, Any],
|
||||
) -> None:
|
||||
"""Store request/response messages to Foundry Memory for future retrieval.
|
||||
|
||||
This method updates the memory store with conversation messages.
|
||||
The update is debounced by the configured update_delay.
|
||||
"""
|
||||
messages_to_store: list[Message] = list(context.input_messages)
|
||||
if context.response and context.response.messages:
|
||||
messages_to_store.extend(context.response.messages)
|
||||
|
||||
# Filter and convert messages to memory update item format
|
||||
items: list[ResponseInputItemParam] = []
|
||||
for message in messages_to_store:
|
||||
if message.role in {"user", "assistant", "system"} and message.text and message.text.strip():
|
||||
if message.role == "user":
|
||||
items.append({"role": "user", "type": "message", "content": message.text})
|
||||
elif message.role == "assistant":
|
||||
items.append({"role": "assistant", "type": "message", "content": message.text})
|
||||
|
||||
if not items:
|
||||
return
|
||||
|
||||
try:
|
||||
# Fire and forget - don't wait for the update to complete
|
||||
update_poller = await self.project_client.beta.memory_stores.begin_update_memories(
|
||||
name=self.memory_store_name,
|
||||
scope=self.scope or context.session_id, # type: ignore[arg-type]
|
||||
items=items,
|
||||
previous_update_id=state.get("previous_update_id"),
|
||||
update_delay=self.update_delay,
|
||||
)
|
||||
# Store the update_id for next incremental update
|
||||
state["previous_update_id"] = update_poller.update_id
|
||||
|
||||
except Exception as e:
|
||||
# Log but don't fail - memory storage is non-critical
|
||||
logger.warning(f"Failed to update memories: {e}")
|
||||
|
||||
|
||||
__all__ = ["FoundryMemoryProvider"]
|
||||
@@ -0,0 +1,49 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import sys
|
||||
from collections.abc import Sequence
|
||||
|
||||
from agent_framework import Content
|
||||
|
||||
if sys.version_info >= (3, 11):
|
||||
from typing import TypedDict # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import TypedDict # type: ignore # pragma: no cover
|
||||
|
||||
logger = logging.getLogger("agent_framework.foundry")
|
||||
|
||||
|
||||
class FoundryProjectSettings(TypedDict, total=False):
|
||||
"""Foundry project settings loaded from FOUNDRY_ environment variables."""
|
||||
|
||||
project_endpoint: str | None
|
||||
|
||||
|
||||
def resolve_file_ids(file_ids: Sequence[str | Content] | None) -> list[str] | None:
|
||||
"""Resolve file IDs from strings or hosted-file Content objects."""
|
||||
if not file_ids:
|
||||
return None
|
||||
|
||||
resolved: list[str] = []
|
||||
for item in file_ids:
|
||||
if isinstance(item, str):
|
||||
if not item:
|
||||
raise ValueError("file_ids must not contain empty strings.")
|
||||
resolved.append(item)
|
||||
elif isinstance(item, Content):
|
||||
if item.type != "hosted_file":
|
||||
raise ValueError(
|
||||
f"Unsupported Content type {item.type!r} for code interpreter file_ids. "
|
||||
"Only Content.from_hosted_file() is supported."
|
||||
)
|
||||
if item.file_id is None:
|
||||
raise ValueError(
|
||||
"Content.from_hosted_file() item is missing a file_id. "
|
||||
"Ensure the Content object has a valid file_id before using it in file_ids."
|
||||
)
|
||||
resolved.append(item.file_id)
|
||||
|
||||
return resolved if resolved else None
|
||||
Reference in New Issue
Block a user