diff --git a/infra/vscode_web/codeSample.py b/infra/vscode_web/codeSample.py index 37224009..6893aef0 100644 --- a/infra/vscode_web/codeSample.py +++ b/infra/vscode_web/codeSample.py @@ -1,5 +1,6 @@ from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential +from azure.ai.agents.models import ListSortOrder project_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), @@ -7,19 +8,25 @@ agent = project_client.agents.get_agent("<%= agentId %>") -thread = project_client.agents.create_thread() +thread = project_client.agents.threads.create() print(f"Created thread, ID: {thread.id}") -message = project_client.agents.create_message( +message = project_client.agents.messages.create( thread_id=thread.id, role="user", content="<%= userMessage %>" ) -run = project_client.agents.create_and_process_run( +run = project_client.agents.runs.create_and_process( thread_id=thread.id, agent_id=agent.id) -messages = project_client.agents.list_messages(thread_id=thread.id) -for text_message in messages.text_messages: - print(text_message.as_dict()) +if run.status == "failed": + print(f"Run failed: {run.last_error}") +else: + messages = project_client.agents.messages.list( + thread_id=thread.id, order=ListSortOrder.ASCENDING) + + for message in messages: + if message.text_messages: + print(f"{message.role}: {message.text_messages[-1].text.value}") diff --git a/infra/vscode_web/requirements.txt b/infra/vscode_web/requirements.txt index 18d6803e..d7ff98e4 100644 --- a/infra/vscode_web/requirements.txt +++ b/infra/vscode_web/requirements.txt @@ -1,3 +1,3 @@ -azure-ai-projects==1.0.0b12 +azure-ai-projects==2.1.0 azure-identity==1.20.0 ansible-core~=2.17.0 \ No newline at end of file diff --git a/src/ContentProcessor/pyproject.toml b/src/ContentProcessor/pyproject.toml index 310524ce..7abbd327 100644 --- a/src/ContentProcessor/pyproject.toml +++ b/src/ContentProcessor/pyproject.toml @@ -5,7 +5,7 @@ description = "Content Process Gold Standard Solution Accelerator - Content Proc readme = "README.md" requires-python = ">=3.12" dependencies = [ - "agent-framework==1.0.0b260127", + "agent-framework==1.1.1", "azure-ai-inference==1.0.0b9", "azure-appconfiguration==1.8.0", "azure-identity==1.26.0b1", diff --git a/src/ContentProcessor/src/libs/agent_framework/agent_builder.py b/src/ContentProcessor/src/libs/agent_framework/agent_builder.py index adb85d58..6125049a 100644 --- a/src/ContentProcessor/src/libs/agent_framework/agent_builder.py +++ b/src/ContentProcessor/src/libs/agent_framework/agent_builder.py @@ -12,13 +12,14 @@ from typing import Any, Literal from agent_framework import ( - ChatAgent, - ChatClientProtocol, - ChatMessageStoreProtocol, + Agent, + AgentMiddleware, + ChatMiddleware, + ChatOptions, ContextProvider, - Middleware, + HistoryProvider, + SupportsChatGetResponse, ToolMode, - ToolProtocol, ) from pydantic import BaseModel @@ -64,7 +65,7 @@ class AgentBuilder: ) """ - def __init__(self, chat_client: ChatClientProtocol): + def __init__(self, chat_client: SupportsChatGetResponse): """Initialize the builder with a chat client. Args: @@ -75,12 +76,15 @@ def __init__(self, chat_client: ChatClientProtocol): self._id: str | None = None self._name: str | None = None self._description: str | None = None - self._chat_message_store_factory: ( - Callable[[], ChatMessageStoreProtocol] | None - ) = None + self._chat_message_store_factory: Callable[[], HistoryProvider] | None = None self._conversation_id: str | None = None self._context_providers: ContextProvider | list[ContextProvider] | None = None - self._middleware: Middleware | list[Middleware] | None = None + self._middleware: ( + AgentMiddleware + | ChatMiddleware + | list[AgentMiddleware | ChatMiddleware] + | None + ) = None self._frequency_penalty: float | None = None self._logit_bias: dict[str | int, float] | None = None self._max_tokens: int | None = None @@ -96,10 +100,10 @@ def __init__(self, chat_client: ChatClientProtocol): ToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None ) = "auto" self._tools: ( - ToolProtocol + Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]] + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]] | None ) = None self._top_p: float | None = None @@ -181,10 +185,10 @@ def with_max_tokens(self, max_tokens: int) -> "AgentBuilder": def with_tools( self, - tools: ToolProtocol + tools: Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]], + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]], ) -> "AgentBuilder": """Set the tools available to the agent. @@ -213,7 +217,10 @@ def with_tool_choice( return self def with_middleware( - self, middleware: Middleware | list[Middleware] + self, + middleware: AgentMiddleware + | ChatMiddleware + | list[AgentMiddleware | ChatMiddleware], ) -> "AgentBuilder": """Set middleware for request/response processing. @@ -385,7 +392,7 @@ def with_store(self, store: bool) -> "AgentBuilder": return self def with_message_store_factory( - self, factory: Callable[[], ChatMessageStoreProtocol] + self, factory: Callable[[], HistoryProvider] ) -> "AgentBuilder": """Set the message store factory. @@ -422,7 +429,7 @@ def with_kwargs(self, **kwargs: Any) -> "AgentBuilder": self._kwargs.update(kwargs) return self - def build(self) -> ChatAgent: + def build(self) -> Agent: """Build and return the configured ChatAgent. Returns: @@ -442,32 +449,52 @@ def build(self) -> ChatAgent: async with agent: response = await agent.run("Hello!") """ - return ChatAgent( - chat_client=self._chat_client, + options: dict[str, Any] = {} + if self._frequency_penalty is not None: + options["frequency_penalty"] = self._frequency_penalty + if self._logit_bias is not None: + options["logit_bias"] = self._logit_bias + if self._max_tokens is not None: + options["max_tokens"] = self._max_tokens + if self._metadata is not None: + options["metadata"] = self._metadata + if self._model_id is not None: + options["model"] = self._model_id + if self._presence_penalty is not None: + options["presence_penalty"] = self._presence_penalty + if self._response_format is not None: + options["response_format"] = self._response_format + if self._seed is not None: + options["seed"] = self._seed + if self._stop is not None: + options["stop"] = self._stop + if self._store is not None: + options["store"] = self._store + if self._temperature is not None: + options["temperature"] = self._temperature + if self._tool_choice is not None: + options["tool_choice"] = self._tool_choice + if self._top_p is not None: + options["top_p"] = self._top_p + if self._user is not None: + options["user"] = self._user + if self._conversation_id is not None: + options["conversation_id"] = self._conversation_id + if self._additional_chat_options: + options.update(self._additional_chat_options) + + default_options: ChatOptions | None = ChatOptions(**options) if options else None + + return Agent( + client=self._chat_client, instructions=self._instructions, id=self._id, name=self._name, description=self._description, - chat_message_store_factory=self._chat_message_store_factory, - conversation_id=self._conversation_id, + tools=self._tools, + default_options=default_options, context_providers=self._context_providers, middleware=self._middleware, - frequency_penalty=self._frequency_penalty, - logit_bias=self._logit_bias, - max_tokens=self._max_tokens, - metadata=self._metadata, - model_id=self._model_id, - presence_penalty=self._presence_penalty, - response_format=self._response_format, - seed=self._seed, - stop=self._stop, - store=self._store, - temperature=self._temperature, - tool_choice=self._tool_choice, - tools=self._tools, - top_p=self._top_p, - user=self._user, - additional_chat_options=self._additional_chat_options, **self._kwargs, ) @@ -477,11 +504,12 @@ def create_agent_by_agentinfo( agent_info: AgentInfo, *, id: str | None = None, - chat_message_store_factory: Callable[[], ChatMessageStoreProtocol] - | None = None, + chat_message_store_factory: Callable[[], HistoryProvider] | None = None, conversation_id: str | None = None, context_providers: ContextProvider | list[ContextProvider] | None = None, - middleware: Middleware | list[Middleware] | None = None, + middleware: ( + AgentMiddleware | ChatMiddleware | list[AgentMiddleware | ChatMiddleware] | None + ) = None, frequency_penalty: float | None = None, logit_bias: dict[str | int, float] | None = None, max_tokens: int | None = None, @@ -497,16 +525,16 @@ def create_agent_by_agentinfo( | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto", - tools: ToolProtocol + tools: Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]] + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]] | None = None, top_p: float | None = None, user: str | None = None, additional_chat_options: dict[str, Any] | None = None, **kwargs: Any, - ) -> ChatAgent: + ) -> Agent: """Create an agent using AgentInfo configuration with full parameter support. This method creates a chat client from the service configuration and then @@ -608,17 +636,18 @@ def create_agent_by_agentinfo( @staticmethod def create_agent( - chat_client: ChatClientProtocol, + chat_client: SupportsChatGetResponse, instructions: str | None = None, *, id: str | None = None, name: str | None = None, description: str | None = None, - chat_message_store_factory: Callable[[], ChatMessageStoreProtocol] - | None = None, + chat_message_store_factory: Callable[[], HistoryProvider] | None = None, conversation_id: str | None = None, context_providers: ContextProvider | list[ContextProvider] | None = None, - middleware: Middleware | list[Middleware] | None = None, + middleware: ( + AgentMiddleware | ChatMiddleware | list[AgentMiddleware | ChatMiddleware] | None + ) = None, frequency_penalty: float | None = None, logit_bias: dict[str | int, float] | None = None, max_tokens: int | None = None, @@ -634,16 +663,16 @@ def create_agent( | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto", - tools: ToolProtocol + tools: Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]] + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]] | None = None, top_p: float | None = None, user: str | None = None, additional_chat_options: dict[str, Any] | None = None, **kwargs: Any, - ) -> ChatAgent: + ) -> Agent: """Create a Chat Client Agent. Factory method that creates a ChatAgent instance with the specified configuration. @@ -745,7 +774,7 @@ def create_agent( temperature=0.7, max_tokens=500, additional_chat_options={ - "reasoning": {"effort": "high", "summary": "concise"} + "reasoning_effort": "high" }, # OpenAI-specific reasoning options ) @@ -758,31 +787,51 @@ def create_agent( ``async with`` to ensure proper initialization and cleanup via the ChatAgent's async context manager protocol. """ - return ChatAgent( - chat_client=chat_client, + options: dict[str, Any] = {} + if frequency_penalty is not None: + options["frequency_penalty"] = frequency_penalty + if logit_bias is not None: + options["logit_bias"] = logit_bias + if max_tokens is not None: + options["max_tokens"] = max_tokens + if metadata is not None: + options["metadata"] = metadata + if model_id is not None: + options["model"] = model_id + if presence_penalty is not None: + options["presence_penalty"] = presence_penalty + if response_format is not None: + options["response_format"] = response_format + if seed is not None: + options["seed"] = seed + if stop is not None: + options["stop"] = stop + if store is not None: + options["store"] = store + if temperature is not None: + options["temperature"] = temperature + if tool_choice is not None: + options["tool_choice"] = tool_choice + if top_p is not None: + options["top_p"] = top_p + if user is not None: + options["user"] = user + if conversation_id is not None: + options["conversation_id"] = conversation_id + if additional_chat_options: + options.update(additional_chat_options) + + default_options: ChatOptions | None = ChatOptions(**options) if options else None + + return Agent( + client=chat_client, instructions=instructions, id=id, name=name, description=description, - chat_message_store_factory=chat_message_store_factory, - conversation_id=conversation_id, + tools=tools, + default_options=default_options, context_providers=context_providers, middleware=middleware, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - max_tokens=max_tokens, - metadata=metadata, - model_id=model_id, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - store=store, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_p=top_p, - user=user, - additional_chat_options=additional_chat_options, **kwargs, ) diff --git a/src/ContentProcessor/src/libs/agent_framework/agent_framework_helper.py b/src/ContentProcessor/src/libs/agent_framework/agent_framework_helper.py index de778b6e..13ad374c 100644 --- a/src/ContentProcessor/src/libs/agent_framework/agent_framework_helper.py +++ b/src/ContentProcessor/src/libs/agent_framework/agent_framework_helper.py @@ -28,11 +28,9 @@ ) if TYPE_CHECKING: - from agent_framework.azure import ( - AzureAIAgentClient, - AzureOpenAIAssistantsClient, - AzureOpenAIChatClient, - AzureOpenAIResponsesClient, + from agent_framework.openai import ( + OpenAIChatClient, + OpenAIChatCompletionClient, ) @@ -142,7 +140,7 @@ def create_client( env_file_path: str | None = None, env_file_encoding: str | None = None, instruction_role: str | None = None, - ) -> "AzureOpenAIChatClient": + ) -> "OpenAIChatCompletionClient": pass @overload @@ -189,7 +187,7 @@ def create_client( async_client: object | None = None, env_file_path: str | None = None, env_file_encoding: str | None = None, - ) -> "AzureOpenAIAssistantsClient": + ) -> Any: pass @overload @@ -211,7 +209,7 @@ def create_client( env_file_path: str | None = None, env_file_encoding: str | None = None, instruction_role: str | None = None, - ) -> "AzureOpenAIResponsesClient": + ) -> "OpenAIChatClient": pass @overload @@ -251,7 +249,7 @@ def create_client( async_credential: object | None = None, env_file_path: str | None = None, env_file_encoding: str | None = None, - ) -> "AzureAIAgentClient": + ) -> Any: pass @staticmethod @@ -384,18 +382,15 @@ def create_client( "OpenAIResponsesClient is not implemented in this context." ) elif client_type == ClientType.AzureOpenAIChatCompletion: - from agent_framework.azure import AzureOpenAIChatClient + from agent_framework.openai import OpenAIChatCompletionClient - return AzureOpenAIChatClient( + return OpenAIChatCompletionClient( + model=deployment_name, api_key=api_key, - deployment_name=deployment_name, - endpoint=endpoint, + azure_endpoint=endpoint, base_url=base_url, api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, + credential=credential or ad_token_provider, default_headers=default_headers, async_client=async_client, env_file_path=env_file_path, @@ -404,15 +399,12 @@ def create_client( ) elif client_type == ClientType.AzureOpenAIChatCompletionWithRetry: return AzureOpenAIChatClientWithRetry( + model=deployment_name, api_key=api_key, - deployment_name=deployment_name, - endpoint=endpoint, + azure_endpoint=endpoint, base_url=base_url, api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, + credential=credential or ad_token_provider, default_headers=default_headers, async_client=async_client, env_file_path=env_file_path, @@ -421,39 +413,20 @@ def create_client( retry_config=retry_config, ) elif client_type == ClientType.AzureOpenAIAssistant: - from agent_framework.azure import AzureOpenAIAssistantsClient - - return AzureOpenAIAssistantsClient( - deployment_name=deployment_name, - assistant_id=assistant_id, - assistant_name=assistant_name, - thread_id=thread_id, - api_key=api_key, - endpoint=endpoint, - base_url=base_url, - api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, - default_headers=default_headers, - async_client=async_client, - env_file_path=env_file_path, - env_file_encoding=env_file_encoding, + raise NotImplementedError( + "AzureOpenAIAssistantsClient has been removed in agent-framework 1.1.1. " + "Use OpenAIChatClient with built-in tools instead." ) elif client_type == ClientType.AzureOpenAIResponse: - from agent_framework.azure import AzureOpenAIResponsesClient + from agent_framework.openai import OpenAIChatClient - return AzureOpenAIResponsesClient( + return OpenAIChatClient( + model=deployment_name, api_key=api_key, - deployment_name=deployment_name, - endpoint=endpoint, + azure_endpoint=endpoint, base_url=base_url, api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, + credential=credential or ad_token_provider, default_headers=default_headers, async_client=async_client, env_file_path=env_file_path, @@ -462,15 +435,12 @@ def create_client( ) elif client_type == ClientType.AzureOpenAIResponseWithRetry: return AzureOpenAIResponseClientWithRetry( + model=deployment_name, api_key=api_key, - deployment_name=deployment_name, - endpoint=endpoint, + azure_endpoint=endpoint, base_url=base_url, api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, + credential=credential or ad_token_provider, default_headers=default_headers, async_client=async_client, env_file_path=env_file_path, @@ -479,18 +449,9 @@ def create_client( retry_config=retry_config, ) elif client_type == ClientType.AzureOpenAIAgent: - from agent_framework.azure import AzureAIAgentClient - - return AzureAIAgentClient( - project_client=project_client, - agent_id=agent_id, - agent_name=agent_name, - thread_id=thread_id, - project_endpoint=project_endpoint, - model_deployment_name=model_deployment_name, - async_credential=async_credential, - env_file_path=env_file_path, - env_file_encoding=env_file_encoding, + raise NotImplementedError( + "AzureAIAgentClient has been removed in agent-framework 1.1.1. " + "Use FoundryChatClient from agent_framework.foundry instead." ) else: raise ValueError(f"Unsupported agent type: {client_type}") diff --git a/src/ContentProcessor/src/libs/agent_framework/agent_info.py b/src/ContentProcessor/src/libs/agent_framework/agent_info.py index cc3c3a82..95b95d89 100644 --- a/src/ContentProcessor/src/libs/agent_framework/agent_info.py +++ b/src/ContentProcessor/src/libs/agent_framework/agent_info.py @@ -9,7 +9,6 @@ from typing import Any, Callable, MutableMapping, Sequence -from agent_framework import ToolProtocol from jinja2 import Template from openai import BaseModel from pydantic import Field @@ -36,10 +35,10 @@ class AgentInfo(BaseModel): agent_instruction: str | None = Field(default=None) agent_framework_helper: AgentFrameworkHelper | None = Field(default=None) tools: ( - ToolProtocol + Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]] + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]] | None ) = Field(default=None) diff --git a/src/ContentProcessor/src/libs/agent_framework/azure_openai_response_retry.py b/src/ContentProcessor/src/libs/agent_framework/azure_openai_response_retry.py index 32b0f187..8212bde2 100644 --- a/src/ContentProcessor/src/libs/agent_framework/azure_openai_response_retry.py +++ b/src/ContentProcessor/src/libs/agent_framework/azure_openai_response_retry.py @@ -11,15 +11,13 @@ from __future__ import annotations -import asyncio -import inspect import logging import os import random from dataclasses import dataclass -from typing import Any, AsyncIterable, MutableSequence +from typing import Any, MutableSequence -from agent_framework.azure import AzureOpenAIChatClient, AzureOpenAIResponsesClient +from agent_framework.openai import OpenAIChatCompletionClient, OpenAIChatClient from tenacity import ( AsyncRetrying, retry_if_exception, @@ -462,7 +460,7 @@ def __call__(self, retry_state) -> float: raise RuntimeError("Retry loop exhausted unexpectedly") -class AzureOpenAIResponseClientWithRetry(AzureOpenAIResponsesClient): +class AzureOpenAIResponseClientWithRetry(OpenAIChatClient): """Azure OpenAI Responses client with 429 retry at the request boundary. Retry is centralized in the client layer (not in orchestrators) by retrying the @@ -484,17 +482,13 @@ async def _inner_get_response( self, *, messages: MutableSequence[Any], - chat_options: Any | None = None, + options: Any | None = None, **kwargs: Any, ) -> Any: parent_inner_get_response = super( AzureOpenAIResponseClientWithRetry, self )._inner_get_response - parent_supports_chat_options = ( - "chat_options" in inspect.signature(parent_inner_get_response).parameters - ) - effective_messages: MutableSequence[Any] | list[Any] = messages if self._context_trim_config.enabled: approx_chars = sum(len(_estimate_message_text(m)) for m in messages) @@ -514,16 +508,9 @@ async def _inner_get_response( ) try: - if parent_supports_chat_options: - return await _retry_call( - lambda: parent_inner_get_response( - messages=effective_messages, chat_options=chat_options, **kwargs - ), - config=self._retry_config, - ) return await _retry_call( lambda: parent_inner_get_response( - messages=effective_messages, **kwargs + messages=effective_messages, options=options, **kwargs ), config=self._retry_config, ) @@ -542,147 +529,18 @@ async def _inner_get_response( len(trimmed), ) return await _retry_call( - ( - ( - lambda: parent_inner_get_response( - messages=trimmed, chat_options=chat_options, **kwargs - ) - ) - if parent_supports_chat_options - else (lambda: parent_inner_get_response(messages=trimmed, **kwargs)) + lambda: parent_inner_get_response( + messages=trimmed, options=options, **kwargs ), config=self._retry_config, ) - async def _inner_get_streaming_response( - self, - *, - messages: MutableSequence[Any], - chat_options: Any | None = None, - **kwargs: Any, - ) -> AsyncIterable[Any]: - # Conservative retry: only retries failures before the first yielded update. - attempts = self._retry_config.max_retries + 1 - - parent_inner_stream = super( - AzureOpenAIResponseClientWithRetry, self - )._inner_get_streaming_response - parent_supports_chat_options = ( - "chat_options" in inspect.signature(parent_inner_stream).parameters - ) - effective_messages: MutableSequence[Any] | list[Any] = messages - if self._context_trim_config.enabled: - approx_chars = sum(len(_estimate_message_text(m)) for m in messages) - if ( - self._context_trim_config.max_total_chars > 0 - and approx_chars > self._context_trim_config.max_total_chars - ): - effective_messages = _trim_messages( - messages, cfg=self._context_trim_config - ) - logger.warning( - "[AOAI_CTX_TRIM] pre-trimmed streaming request messages: approx_chars=%s -> %s; count=%s -> %s", - approx_chars, - sum(len(_estimate_message_text(m)) for m in effective_messages), - len(messages), - len(effective_messages), - ) - - for attempt_index in range(attempts): - if parent_supports_chat_options: - stream = parent_inner_stream( - messages=effective_messages, chat_options=chat_options, **kwargs - ) - else: - stream = parent_inner_stream(messages=effective_messages, **kwargs) - - iterator = stream.__aiter__() - try: - first = await iterator.__anext__() - - async def _tail(): - yield first - async for item in iterator: - yield item - - async for item in _tail(): - yield item - return - except StopAsyncIteration: - return - except Exception as e: - close = getattr(stream, "aclose", None) - if callable(close): - try: - await close() - except Exception as close_exc: - # Best-effort stream cleanup: ignore close failures so we preserve - # the original exception/retry path. - logger.debug( - "[AOAI_RETRY_STREAM] ignoring stream close failure during retry handling: %s", - _format_exc_brief(close_exc) - if isinstance(close_exc, BaseException) - else str(close_exc), - ) - - # One-shot retry for context-length failures. - if ( - self._context_trim_config.enabled - and self._context_trim_config.retry_on_context_error - and _looks_like_context_length(e) - ): - trimmed = _trim_messages(messages, cfg=self._context_trim_config) - logger.warning( - "[AOAI_CTX_TRIM_STREAM] retrying after context-length error; count=%s -> %s", - len(messages), - len(trimmed), - ) - effective_messages = trimmed - if attempt_index >= attempts - 1: - # No more retries available. - raise - continue - - if not _is_transient_error(e) or attempt_index >= attempts - 1: - if _is_transient_error(e): - logger.warning( - "[AOAI_RETRY_STREAM] giving up after %s/%s attempts; error=%s", - attempt_index + 1, - attempts, - _format_exc_brief(e) - if isinstance(e, BaseException) - else str(e), - ) - raise - - retry_after = _try_get_retry_after_seconds(e) - if retry_after is not None and retry_after >= 0: - delay = retry_after - else: - delay = self._retry_config.base_delay_seconds * (2**attempt_index) - delay = min(delay, self._retry_config.max_delay_seconds) - delay = delay + random.uniform(0.0, 0.25 * max(delay, 0.1)) - - status = getattr(e, "status_code", None) or getattr(e, "status", None) - logger.warning( - "[AOAI_RETRY_STREAM] attempt %s/%s; sleeping=%ss; retry_after=%s; status=%s; error=%s", - attempt_index + 1, - attempts, - round(float(delay), 3), - None if retry_after is None else round(float(retry_after), 3), - status, - _format_exc_brief(e) if isinstance(e, BaseException) else str(e), - ) - - await asyncio.sleep(delay) - - -class AzureOpenAIChatClientWithRetry(AzureOpenAIChatClient): +class AzureOpenAIChatClientWithRetry(OpenAIChatCompletionClient): """Azure OpenAI Chat client with 429 retry at the request boundary. This wraps the underlying chat-completions call used by Agent Framework by overriding - the internal `_inner_get_response` / `_inner_get_streaming_response` methods. + the internal `_inner_get_response` method. """ def __init__( @@ -752,117 +610,3 @@ async def _inner_get_response( ), config=self._retry_config, ) - - async def _inner_get_streaming_response( - self, - *, - messages: MutableSequence[Any], - options: dict[str, Any], - **kwargs: Any, - ) -> AsyncIterable[Any]: - # Conservative retry: only retries failures before the first yielded update. - attempts = self._retry_config.max_retries + 1 - - parent_inner_stream = super( - AzureOpenAIChatClientWithRetry, self - )._inner_get_streaming_response - - effective_messages: MutableSequence[Any] | list[Any] = messages - if self._context_trim_config.enabled: - approx_chars = sum(len(_estimate_message_text(m)) for m in messages) - if ( - self._context_trim_config.max_total_chars > 0 - and approx_chars > self._context_trim_config.max_total_chars - ): - effective_messages = _trim_messages( - messages, cfg=self._context_trim_config - ) - logger.warning( - "[AOAI_CTX_TRIM] pre-trimmed streaming chat request messages: approx_chars=%s -> %s; count=%s -> %s", - approx_chars, - sum(len(_estimate_message_text(m)) for m in effective_messages), - len(messages), - len(effective_messages), - ) - - for attempt_index in range(attempts): - stream = parent_inner_stream( - messages=effective_messages, options=options, **kwargs - ) - - iterator = stream.__aiter__() - try: - first = await iterator.__anext__() - - async def _tail(): - yield first - async for item in iterator: - yield item - - async for item in _tail(): - yield item - return - except StopAsyncIteration: - return - except Exception as e: - close = getattr(stream, "aclose", None) - if callable(close): - try: - await close() - except Exception as close_error: - # Intentionally suppress close-time failures so we do not - # mask the original streaming exception that triggered retry handling. - logger.debug( - "[AOAI_RETRY_STREAM] ignoring stream close failure during error handling", - exc_info=close_error, - ) - - # One-shot retry for context-length failures. - if ( - self._context_trim_config.enabled - and self._context_trim_config.retry_on_context_error - and _looks_like_context_length(e) - ): - trimmed = _trim_messages(messages, cfg=self._context_trim_config) - logger.warning( - "[AOAI_CTX_TRIM_STREAM] retrying chat stream after context-length error; count=%s -> %s", - len(messages), - len(trimmed), - ) - effective_messages = trimmed - if attempt_index >= attempts - 1: - raise - continue - - if not _is_transient_error(e) or attempt_index >= attempts - 1: - if _is_transient_error(e): - logger.warning( - "[AOAI_RETRY_STREAM] giving up after %s/%s attempts; error=%s", - attempt_index + 1, - attempts, - _format_exc_brief(e) - if isinstance(e, BaseException) - else str(e), - ) - raise - - retry_after = _try_get_retry_after_seconds(e) - if retry_after is not None and retry_after >= 0: - delay = retry_after - else: - delay = self._retry_config.base_delay_seconds * (2**attempt_index) - delay = min(delay, self._retry_config.max_delay_seconds) - delay = delay + random.uniform(0.0, 0.25 * max(delay, 0.1)) - - status = getattr(e, "status_code", None) or getattr(e, "status", None) - logger.warning( - "[AOAI_RETRY_STREAM] chat attempt %s/%s; sleeping=%ss; retry_after=%s; status=%s; error=%s", - attempt_index + 1, - attempts, - round(float(delay), 3), - None if retry_after is None else round(float(retry_after), 3), - status, - _format_exc_brief(e) if isinstance(e, BaseException) else str(e), - ) - - await asyncio.sleep(delay) diff --git a/src/ContentProcessor/src/libs/pipeline/handlers/map_handler.py b/src/ContentProcessor/src/libs/pipeline/handlers/map_handler.py index f3f20cb3..54bf352d 100644 --- a/src/ContentProcessor/src/libs/pipeline/handlers/map_handler.py +++ b/src/ContentProcessor/src/libs/pipeline/handlers/map_handler.py @@ -14,7 +14,7 @@ import os from typing import Literal -from agent_framework import ChatMessage, Content +from agent_framework import Content, Message from pdf2image import convert_from_bytes from 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a/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_builder.py +++ b/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_builder.py @@ -34,14 +34,14 @@ from typing import Any, Literal from agent_framework import ( - AggregateContextProvider, - ChatAgent, - ChatClientProtocol, - ChatMessageStoreProtocol, + Agent, + AgentMiddleware, + ChatMiddleware, + ChatOptions, ContextProvider, - Middleware, + HistoryProvider, + SupportsChatGetResponse, ToolMode, - ToolProtocol, ) from pydantic import BaseModel @@ -86,7 +86,7 @@ class AgentBuilder: ) """ - def __init__(self, chat_client: ChatClientProtocol): + def __init__(self, chat_client: SupportsChatGetResponse): """Initialize the builder with a chat client and default-None fields. All configuration is stored as private attributes. None of them are @@ -102,14 +102,15 @@ def __init__(self, chat_client: ChatClientProtocol): self._id: str | None = None self._name: str | None = None self._description: str | None = None - self._chat_message_store_factory: ( - Callable[[], ChatMessageStoreProtocol] | None - ) = None + self._chat_message_store_factory: Callable[[], HistoryProvider] | None = None self._conversation_id: str | None = None - self._context_providers: ( - ContextProvider | list[ContextProvider] | AggregateContextProvider | None + self._context_providers: ContextProvider | list[ContextProvider] | None = None + self._middleware: ( + AgentMiddleware + | ChatMiddleware + | list[AgentMiddleware | ChatMiddleware] + | None ) = None - self._middleware: Middleware | list[Middleware] | None = None self._frequency_penalty: float | None = None self._logit_bias: dict[str | int, float] | None = None self._max_tokens: int | None = None @@ -125,10 +126,10 @@ def __init__(self, chat_client: ChatClientProtocol): ToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None ) = "auto" self._tools: ( - ToolProtocol + Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]] + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]] | None ) = None self._top_p: float | None = None @@ -210,10 +211,10 @@ def with_max_tokens(self, max_tokens: int) -> "AgentBuilder": def with_tools( self, - tools: ToolProtocol + tools: Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]], + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]], ) -> "AgentBuilder": """Set the tools available to the agent. @@ -242,7 +243,10 @@ def with_tool_choice( return self def with_middleware( - self, middleware: Middleware | list[Middleware] + self, + middleware: AgentMiddleware + | ChatMiddleware + | list[AgentMiddleware | ChatMiddleware], ) -> "AgentBuilder": """Set middleware for request/response processing. @@ -257,9 +261,7 @@ def with_middleware( def with_context_providers( self, - context_providers: ContextProvider - | list[ContextProvider] - | AggregateContextProvider, + context_providers: ContextProvider | list[ContextProvider], ) -> "AgentBuilder": """Set context providers for additional conversation context. @@ -417,7 +419,7 @@ def with_store(self, store: bool) -> "AgentBuilder": return self def with_message_store_factory( - self, factory: Callable[[], ChatMessageStoreProtocol] + self, factory: Callable[[], HistoryProvider] ) -> "AgentBuilder": """Set the message store factory. @@ -454,7 +456,7 @@ def with_kwargs(self, **kwargs: Any) -> "AgentBuilder": self._kwargs.update(kwargs) return self - def build(self) -> ChatAgent: + def build(self) -> Agent: """Construct a ``ChatAgent`` from the accumulated configuration. Processing steps: @@ -482,32 +484,52 @@ def build(self) -> ChatAgent: async with agent: response = await agent.run("Hello!") """ - return ChatAgent( - chat_client=self._chat_client, + options: dict[str, Any] = {} + if self._frequency_penalty is not None: + options["frequency_penalty"] = self._frequency_penalty + if self._logit_bias is not None: + options["logit_bias"] = self._logit_bias + if self._max_tokens is not None: + options["max_tokens"] = self._max_tokens + if self._metadata is not None: + options["metadata"] = self._metadata + if self._model_id is not None: + options["model"] = self._model_id + if self._presence_penalty is not None: + options["presence_penalty"] = self._presence_penalty + if self._response_format is not None: + options["response_format"] = self._response_format + if self._seed is not None: + options["seed"] = self._seed + if self._stop is not None: + options["stop"] = self._stop + if self._store is not None: + options["store"] = self._store + if self._temperature is not None: + options["temperature"] = self._temperature + if self._tool_choice is not None: + options["tool_choice"] = self._tool_choice + if self._top_p is not None: + options["top_p"] = self._top_p + if self._user is not None: + options["user"] = self._user + if self._conversation_id is not None: + options["conversation_id"] = self._conversation_id + if self._additional_chat_options: + options.update(self._additional_chat_options) + + default_options: ChatOptions | None = ChatOptions(**options) if options else None + + return Agent( + client=self._chat_client, instructions=self._instructions, id=self._id, name=self._name, description=self._description, - chat_message_store_factory=self._chat_message_store_factory, - conversation_id=self._conversation_id, + tools=self._tools, + default_options=default_options, context_providers=self._context_providers, middleware=self._middleware, - frequency_penalty=self._frequency_penalty, - logit_bias=self._logit_bias, - max_tokens=self._max_tokens, - metadata=self._metadata, - model_id=self._model_id, - presence_penalty=self._presence_penalty, - response_format=self._response_format, - seed=self._seed, - stop=self._stop, - store=self._store, - temperature=self._temperature, - tool_choice=self._tool_choice, - tools=self._tools, - top_p=self._top_p, - user=self._user, - additional_chat_options=self._additional_chat_options, **self._kwargs, ) @@ -517,14 +539,12 @@ def create_agent_by_agentinfo( agent_info: AgentInfo, *, id: str | None = None, - chat_message_store_factory: Callable[[], ChatMessageStoreProtocol] - | None = None, + chat_message_store_factory: Callable[[], HistoryProvider] | None = None, conversation_id: str | None = None, - context_providers: ContextProvider - | list[ContextProvider] - | AggregateContextProvider - | None = None, - middleware: Middleware | list[Middleware] | None = None, + context_providers: ContextProvider | list[ContextProvider] | None = None, + middleware: ( + AgentMiddleware | ChatMiddleware | list[AgentMiddleware | ChatMiddleware] | None + ) = None, frequency_penalty: float | None = None, logit_bias: dict[str | int, float] | None = None, max_tokens: int | None = None, @@ -540,16 +560,16 @@ def create_agent_by_agentinfo( | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto", - tools: ToolProtocol + tools: Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]] + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]] | None = None, top_p: float | None = None, user: str | None = None, additional_chat_options: dict[str, Any] | None = None, **kwargs: Any, - ) -> ChatAgent: + ) -> Agent: """Create a ``ChatAgent`` from an ``AgentInfo`` metadata bundle. This factory resolves the chat client automatically by reading the @@ -664,20 +684,18 @@ def create_agent_by_agentinfo( @staticmethod def create_agent( - chat_client: ChatClientProtocol, + chat_client: SupportsChatGetResponse, instructions: str | None = None, *, id: str | None = None, name: str | None = None, description: str | None = None, - chat_message_store_factory: Callable[[], ChatMessageStoreProtocol] - | None = None, + chat_message_store_factory: Callable[[], HistoryProvider] | None = None, conversation_id: str | None = None, - context_providers: ContextProvider - | list[ContextProvider] - | AggregateContextProvider - | None = None, - middleware: Middleware | list[Middleware] | None = None, + context_providers: ContextProvider | list[ContextProvider] | None = None, + middleware: ( + AgentMiddleware | ChatMiddleware | list[AgentMiddleware | ChatMiddleware] | None + ) = None, frequency_penalty: float | None = None, logit_bias: dict[str | int, float] | None = None, max_tokens: int | None = None, @@ -693,16 +711,16 @@ def create_agent( | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto", - tools: ToolProtocol + tools: Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]] + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]] | None = None, top_p: float | None = None, user: str | None = None, additional_chat_options: dict[str, Any] | None = None, **kwargs: Any, - ) -> ChatAgent: + ) -> Agent: """Create a Chat Client Agent. Factory method that creates a ChatAgent instance with the specified configuration. @@ -804,7 +822,7 @@ def create_agent( temperature=0.7, max_tokens=500, additional_chat_options={ - "reasoning": {"effort": "high", "summary": "concise"} + "reasoning_effort": "high" }, # OpenAI-specific reasoning options ) @@ -817,31 +835,51 @@ def create_agent( ``async with`` to ensure proper initialization and cleanup via the ChatAgent's async context manager protocol. """ - return ChatAgent( - chat_client=chat_client, + options: dict[str, Any] = {} + if frequency_penalty is not None: + options["frequency_penalty"] = frequency_penalty + if logit_bias is not None: + options["logit_bias"] = logit_bias + if max_tokens is not None: + options["max_tokens"] = max_tokens + if metadata is not None: + options["metadata"] = metadata + if model_id is not None: + options["model"] = model_id + if presence_penalty is not None: + options["presence_penalty"] = presence_penalty + if response_format is not None: + options["response_format"] = response_format + if seed is not None: + options["seed"] = seed + if stop is not None: + options["stop"] = stop + if store is not None: + options["store"] = store + if temperature is not None: + options["temperature"] = temperature + if tool_choice is not None: + options["tool_choice"] = tool_choice + if top_p is not None: + options["top_p"] = top_p + if user is not None: + options["user"] = user + if conversation_id is not None: + options["conversation_id"] = conversation_id + if additional_chat_options: + options.update(additional_chat_options) + + default_options: ChatOptions | None = ChatOptions(**options) if options else None + + return Agent( + client=chat_client, instructions=instructions, id=id, name=name, description=description, - chat_message_store_factory=chat_message_store_factory, - conversation_id=conversation_id, + tools=tools, + default_options=default_options, context_providers=context_providers, middleware=middleware, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - max_tokens=max_tokens, - metadata=metadata, - model_id=model_id, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - store=store, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_p=top_p, - user=user, - additional_chat_options=additional_chat_options, **kwargs, ) diff --git a/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_framework_helper.py b/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_framework_helper.py index e2c9c9fb..a185105f 100644 --- a/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_framework_helper.py +++ b/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_framework_helper.py @@ -28,11 +28,9 @@ ) if TYPE_CHECKING: - from agent_framework.azure import ( - AzureAIAgentClient, - AzureOpenAIAssistantsClient, - AzureOpenAIChatClient, - AzureOpenAIResponsesClient, + from agent_framework.openai import ( + OpenAIChatClient, + OpenAIChatCompletionClient, ) @@ -142,7 +140,7 @@ def create_client( # noqa: E704 env_file_path: str | None = None, env_file_encoding: str | None = None, instruction_role: str | None = None, - ) -> "AzureOpenAIChatClient": + ) -> "OpenAIChatCompletionClient": pass @overload @@ -189,7 +187,7 @@ def create_client( # noqa: E704 async_client: object | None = None, env_file_path: str | None = None, env_file_encoding: str | None = None, - ) -> "AzureOpenAIAssistantsClient": + ) -> Any: raise NotImplementedError @overload @@ -211,7 +209,7 @@ def create_client( # noqa: E704 env_file_path: str | None = None, env_file_encoding: str | None = None, instruction_role: str | None = None, - ) -> "AzureOpenAIResponsesClient": + ) -> "OpenAIChatClient": pass @overload @@ -251,7 +249,7 @@ def create_client( # noqa: E704 async_credential: object | None = None, env_file_path: str | None = None, env_file_encoding: str | None = None, - ) -> "AzureAIAgentClient": + ) -> Any: pass @staticmethod @@ -384,18 +382,15 @@ def create_client( "OpenAIResponsesClient is not implemented in this context." ) elif client_type == ClientType.AzureOpenAIChatCompletion: - from agent_framework.azure import AzureOpenAIChatClient + from agent_framework.openai import OpenAIChatCompletionClient - return AzureOpenAIChatClient( + return OpenAIChatCompletionClient( + model=deployment_name, api_key=api_key, - deployment_name=deployment_name, - endpoint=endpoint, + azure_endpoint=endpoint, base_url=base_url, api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, + credential=credential or ad_token_provider, default_headers=default_headers, async_client=async_client, env_file_path=env_file_path, @@ -404,15 +399,12 @@ def create_client( ) elif client_type == ClientType.AzureOpenAIChatCompletionWithRetry: return AzureOpenAIChatClientWithRetry( + model=deployment_name, api_key=api_key, - deployment_name=deployment_name, - endpoint=endpoint, + azure_endpoint=endpoint, base_url=base_url, api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, + credential=credential or ad_token_provider, default_headers=default_headers, async_client=async_client, env_file_path=env_file_path, @@ -421,39 +413,20 @@ def create_client( retry_config=retry_config, ) elif client_type == ClientType.AzureOpenAIAssistant: - from agent_framework.azure import AzureOpenAIAssistantsClient - - return AzureOpenAIAssistantsClient( - deployment_name=deployment_name, - assistant_id=assistant_id, - assistant_name=assistant_name, - thread_id=thread_id, - api_key=api_key, - endpoint=endpoint, - base_url=base_url, - api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, - default_headers=default_headers, - async_client=async_client, - env_file_path=env_file_path, - env_file_encoding=env_file_encoding, + raise NotImplementedError( + "AzureOpenAIAssistantsClient has been removed in agent-framework 1.1.1. " + "Use OpenAIChatClient with built-in tools instead." ) elif client_type == ClientType.AzureOpenAIResponse: - from agent_framework.azure import AzureOpenAIResponsesClient + from agent_framework.openai import OpenAIChatClient - return AzureOpenAIResponsesClient( + return OpenAIChatClient( + model=deployment_name, api_key=api_key, - deployment_name=deployment_name, - endpoint=endpoint, + azure_endpoint=endpoint, base_url=base_url, api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, + credential=credential or ad_token_provider, default_headers=default_headers, async_client=async_client, env_file_path=env_file_path, @@ -462,15 +435,12 @@ def create_client( ) elif client_type == ClientType.AzureOpenAIResponseWithRetry: return AzureOpenAIResponseClientWithRetry( + model=deployment_name, api_key=api_key, - deployment_name=deployment_name, - endpoint=endpoint, + azure_endpoint=endpoint, base_url=base_url, api_version=api_version, - ad_token=ad_token, - ad_token_provider=ad_token_provider, - token_endpoint=token_endpoint, - credential=credential, + credential=credential or ad_token_provider, default_headers=default_headers, async_client=async_client, env_file_path=env_file_path, @@ -479,18 +449,9 @@ def create_client( retry_config=retry_config, ) elif client_type == ClientType.AzureOpenAIAgent: - from agent_framework.azure import AzureAIAgentClient - - return AzureAIAgentClient( - project_client=project_client, - agent_id=agent_id, - agent_name=agent_name, - thread_id=thread_id, - project_endpoint=project_endpoint, - model_deployment_name=model_deployment_name, - async_credential=async_credential, - env_file_path=env_file_path, - env_file_encoding=env_file_encoding, + raise NotImplementedError( + "AzureAIAgentClient has been removed in agent-framework 1.1.1. " + "Use FoundryChatClient from agent_framework.foundry instead." ) else: raise ValueError(f"Unsupported agent type: {client_type}") diff --git a/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_info.py b/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_info.py index 6d66daee..f8fece3c 100644 --- a/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_info.py +++ b/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_info.py @@ -21,7 +21,6 @@ from typing import Any, Callable, MutableMapping, Sequence -from agent_framework import ToolProtocol from jinja2 import Template from openai import BaseModel from pydantic import Field @@ -58,10 +57,10 @@ class AgentInfo(BaseModel): agent_instruction: str | None = Field(default=None) agent_framework_helper: AgentFrameworkHelper | None = Field(default=None) tools: ( - ToolProtocol + Any | Callable[..., Any] | MutableMapping[str, Any] - | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]] + | Sequence[Any | Callable[..., Any] | MutableMapping[str, Any]] | None ) = Field(default=None) diff --git a/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_speaking_capture.py b/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_speaking_capture.py index 8722b953..d0c96854 100644 --- a/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_speaking_capture.py +++ b/src/ContentProcessorWorkflow/src/libs/agent_framework/agent_speaking_capture.py @@ -36,7 +36,7 @@ from datetime import datetime from typing import Any, Callable, Optional -from agent_framework import AgentMiddleware, AgentRunContext +from agent_framework import AgentContext, AgentMiddleware logger = logging.getLogger(__name__) @@ -105,7 +105,7 @@ def __init__( str, list[str] ] = {} # Buffer for streaming responses - async def process(self, context: AgentRunContext, next): + async def process(self, context: AgentContext, next): """Intercept an agent invocation, delegate execution, then capture the response. Processing steps: diff --git a/src/ContentProcessorWorkflow/src/libs/agent_framework/azure_openai_response_retry.py b/src/ContentProcessorWorkflow/src/libs/agent_framework/azure_openai_response_retry.py index e3f74fcf..6a3d9e9d 100644 --- a/src/ContentProcessorWorkflow/src/libs/agent_framework/azure_openai_response_retry.py +++ b/src/ContentProcessorWorkflow/src/libs/agent_framework/azure_openai_response_retry.py @@ -11,15 +11,13 @@ from __future__ import annotations -import asyncio -import inspect import logging import os import random from dataclasses import dataclass -from typing import Any, AsyncIterable, MutableSequence +from typing import Any, MutableSequence -from agent_framework.azure import AzureOpenAIChatClient, AzureOpenAIResponsesClient +from agent_framework.openai import OpenAIChatCompletionClient, OpenAIChatClient from tenacity import ( AsyncRetrying, retry_if_exception, @@ -251,11 +249,9 @@ def _set_message_text(message: Any, new_text: str) -> Any: try: # Dynamically import TextContent to avoid a hard dependency on # a specific agent_framework version. - from agent_framework._types import ( - TextContent, # type: ignore[import-untyped] - ) + from agent_framework._types import Content - message.contents = [TextContent(text=new_text)] + message.contents = [Content.from_text(new_text)] return message except Exception: # If import or mutation fails, try the generic path below. @@ -525,7 +521,7 @@ def __call__(self, retry_state) -> float: raise RuntimeError("Retry loop exhausted unexpectedly") -class AzureOpenAIResponseClientWithRetry(AzureOpenAIResponsesClient): +class AzureOpenAIResponseClientWithRetry(OpenAIChatClient): """Azure OpenAI Responses client with 429 retry at the request boundary. Retry is centralized in the client layer (not in orchestrators) by retrying the @@ -547,17 +543,13 @@ async def _inner_get_response( self, *, messages: MutableSequence[Any], - chat_options: Any | None = None, + options: Any | None = None, **kwargs: Any, ) -> Any: parent_inner_get_response = super( AzureOpenAIResponseClientWithRetry, self )._inner_get_response - parent_supports_chat_options = ( - "chat_options" in inspect.signature(parent_inner_get_response).parameters - ) - effective_messages: MutableSequence[Any] | list[Any] = messages if self._context_trim_config.enabled: approx_chars = sum(len(_estimate_message_text(m)) for m in messages) @@ -577,16 +569,9 @@ async def _inner_get_response( ) try: - if parent_supports_chat_options: - return await _retry_call( - lambda: parent_inner_get_response( - messages=effective_messages, chat_options=chat_options, **kwargs - ), - config=self._retry_config, - ) return await _retry_call( lambda: parent_inner_get_response( - messages=effective_messages, **kwargs + messages=effective_messages, options=options, **kwargs ), config=self._retry_config, ) @@ -605,143 +590,14 @@ async def _inner_get_response( len(trimmed), ) return await _retry_call( - ( - ( - lambda: parent_inner_get_response( - messages=trimmed, chat_options=chat_options, **kwargs - ) - ) - if parent_supports_chat_options - else (lambda: parent_inner_get_response(messages=trimmed, **kwargs)) + lambda: parent_inner_get_response( + messages=trimmed, options=options, **kwargs ), config=self._retry_config, ) - async def _inner_get_streaming_response( - self, - *, - messages: MutableSequence[Any], - chat_options: Any | None = None, - **kwargs: Any, - ) -> AsyncIterable[Any]: - # Conservative retry: only retries failures before the first yielded update. - attempts = self._retry_config.max_retries + 1 - - parent_inner_stream = super( - AzureOpenAIResponseClientWithRetry, self - )._inner_get_streaming_response - parent_supports_chat_options = ( - "chat_options" in inspect.signature(parent_inner_stream).parameters - ) - - effective_messages: MutableSequence[Any] | list[Any] = messages - if self._context_trim_config.enabled: - approx_chars = sum(len(_estimate_message_text(m)) for m in messages) - if ( - self._context_trim_config.max_total_chars > 0 - and approx_chars > self._context_trim_config.max_total_chars - ): - effective_messages = _trim_messages( - messages, cfg=self._context_trim_config - ) - logger.warning( - "[AOAI_CTX_TRIM] pre-trimmed streaming request messages: approx_chars=%s -> %s; count=%s -> %s", - approx_chars, - sum(len(_estimate_message_text(m)) for m in effective_messages), - len(messages), - len(effective_messages), - ) - - for attempt_index in range(attempts): - if parent_supports_chat_options: - stream = parent_inner_stream( - messages=effective_messages, chat_options=chat_options, **kwargs - ) - else: - stream = parent_inner_stream(messages=effective_messages, **kwargs) - iterator = stream.__aiter__() - try: - first = await iterator.__anext__() - - async def _tail(): - yield first - async for item in iterator: - yield item - - async for item in _tail(): - yield item - return - except StopAsyncIteration: - return - except Exception as e: - close = getattr(stream, "aclose", None) - if callable(close): - try: - await close() - except Exception as close_error: - # Best-effort cleanup: ignore close failures so we preserve - # retry/original-error handling behavior. - logger.debug( - "[AOAI_RETRY_STREAM] ignored stream close error during cleanup: %s", - _format_exc_brief(close_error) - if isinstance(close_error, BaseException) - else str(close_error), - ) - - # One-shot retry for context-length failures. - if ( - self._context_trim_config.enabled - and self._context_trim_config.retry_on_context_error - and _looks_like_context_length(e) - ): - trimmed = _trim_messages(messages, cfg=self._context_trim_config) - logger.warning( - "[AOAI_CTX_TRIM_STREAM] retrying after context-length error; count=%s -> %s", - len(messages), - len(trimmed), - ) - effective_messages = trimmed - if attempt_index >= attempts - 1: - # No more retries available. - raise - continue - - if not _is_transient_error(e) or attempt_index >= attempts - 1: - if _is_transient_error(e): - logger.warning( - "[AOAI_RETRY_STREAM] giving up after %s/%s attempts; error=%s", - attempt_index + 1, - attempts, - _format_exc_brief(e) - if isinstance(e, BaseException) - else str(e), - ) - raise - - retry_after = _try_get_retry_after_seconds(e) - if retry_after is not None and retry_after >= 0: - delay = retry_after - else: - delay = self._retry_config.base_delay_seconds * (2**attempt_index) - delay = min(delay, self._retry_config.max_delay_seconds) - delay = delay + random.uniform(0.0, 0.25 * max(delay, 0.1)) - - status = getattr(e, "status_code", None) or getattr(e, "status", None) - logger.warning( - "[AOAI_RETRY_STREAM] attempt %s/%s; sleeping=%ss; retry_after=%s; status=%s; error=%s", - attempt_index + 1, - attempts, - round(float(delay), 3), - None if retry_after is None else round(float(retry_after), 3), - status, - _format_exc_brief(e) if isinstance(e, BaseException) else str(e), - ) - - await asyncio.sleep(delay) - - -class AzureOpenAIChatClientWithRetry(AzureOpenAIChatClient): +class AzureOpenAIChatClientWithRetry(OpenAIChatCompletionClient): """Azure OpenAI Chat client with 429 retry at the request boundary. This wraps the underlying chat-completions call used by Agent Framework by overriding @@ -763,7 +619,7 @@ async def _inner_get_response( self, *, messages: MutableSequence[Any], - chat_options: Any | None = None, + options: Any | None = None, **kwargs: Any, ) -> Any: parent_inner_get_response = super( @@ -791,7 +647,7 @@ async def _inner_get_response( try: return await _retry_call( lambda: parent_inner_get_response( - messages=effective_messages, chat_options=chat_options, **kwargs + messages=effective_messages, options=options, **kwargs ), config=self._retry_config, ) @@ -811,121 +667,7 @@ async def _inner_get_response( ) return await _retry_call( lambda: parent_inner_get_response( - messages=trimmed, chat_options=chat_options, **kwargs + messages=trimmed, options=options, **kwargs ), config=self._retry_config, ) - - async def _inner_get_streaming_response( - self, - *, - messages: MutableSequence[Any], - chat_options: Any | None = None, - **kwargs: Any, - ) -> AsyncIterable[Any]: - # Conservative retry: only retries failures before the first yielded update. - attempts = self._retry_config.max_retries + 1 - - parent_inner_stream = super( - AzureOpenAIChatClientWithRetry, self - )._inner_get_streaming_response - - effective_messages: MutableSequence[Any] | list[Any] = messages - if self._context_trim_config.enabled: - approx_chars = sum(len(_estimate_message_text(m)) for m in messages) - if ( - self._context_trim_config.max_total_chars > 0 - and approx_chars > self._context_trim_config.max_total_chars - ): - effective_messages = _trim_messages( - messages, cfg=self._context_trim_config - ) - logger.warning( - "[AOAI_CTX_TRIM] pre-trimmed streaming chat request messages: approx_chars=%s -> %s; count=%s -> %s", - approx_chars, - sum(len(_estimate_message_text(m)) for m in effective_messages), - len(messages), - len(effective_messages), - ) - - for attempt_index in range(attempts): - stream = parent_inner_stream( - messages=effective_messages, chat_options=chat_options, **kwargs - ) - - iterator = stream.__aiter__() - try: - first = await iterator.__anext__() - - async def _tail(): - yield first - async for item in iterator: - yield item - - async for item in _tail(): - yield item - return - except StopAsyncIteration: - return - except Exception as e: - close = getattr(stream, "aclose", None) - if callable(close): - try: - await close() - except Exception as close_err: - logger.debug( - "[AOAI_RETRY_STREAM] ignoring stream close error during cleanup: %s", - _format_exc_brief(close_err) - if isinstance(close_err, BaseException) - else str(close_err), - ) - - # One-shot retry for context-length failures. - if ( - self._context_trim_config.enabled - and self._context_trim_config.retry_on_context_error - and _looks_like_context_length(e) - ): - trimmed = _trim_messages(messages, cfg=self._context_trim_config) - logger.warning( - "[AOAI_CTX_TRIM_STREAM] retrying chat stream after context-length error; count=%s -> %s", - len(messages), - len(trimmed), - ) - effective_messages = trimmed - if attempt_index >= attempts - 1: - raise - continue - - if not _is_transient_error(e) or attempt_index >= attempts - 1: - if _is_transient_error(e): - logger.warning( - "[AOAI_RETRY_STREAM] giving up after %s/%s attempts; error=%s", - attempt_index + 1, - attempts, - _format_exc_brief(e) - if isinstance(e, BaseException) - else str(e), - ) - raise - - retry_after = _try_get_retry_after_seconds(e) - if retry_after is not None and retry_after >= 0: - delay = retry_after - else: - delay = self._retry_config.base_delay_seconds * (2**attempt_index) - delay = min(delay, self._retry_config.max_delay_seconds) - delay = delay + random.uniform(0.0, 0.25 * max(delay, 0.1)) - - status = getattr(e, "status_code", None) or getattr(e, "status", None) - logger.warning( - "[AOAI_RETRY_STREAM] chat attempt %s/%s; sleeping=%ss; retry_after=%s; status=%s; error=%s", - attempt_index + 1, - attempts, - round(float(delay), 3), - None if retry_after is None else round(float(retry_after), 3), - status, - _format_exc_brief(e) if isinstance(e, BaseException) else str(e), - ) - - await asyncio.sleep(delay) diff --git a/src/ContentProcessorWorkflow/src/libs/agent_framework/groupchat_orchestrator.py b/src/ContentProcessorWorkflow/src/libs/agent_framework/groupchat_orchestrator.py index b0ddd29c..426e2827 100644 --- a/src/ContentProcessorWorkflow/src/libs/agent_framework/groupchat_orchestrator.py +++ b/src/ContentProcessorWorkflow/src/libs/agent_framework/groupchat_orchestrator.py @@ -52,22 +52,19 @@ from typing import Any, Awaitable, Callable, Generic, Mapping, Sequence, TypeVar from agent_framework import ( - AgentProtocol, - AgentRunUpdateEvent, - ChatAgent, - ChatMessage, + Agent, + AgentResponseUpdate, + ChatOptions, Executor, - GroupChatBuilder, - ManagerSelectionResponse, - Role, + Message, + SupportsAgentRun, Workflow, - WorkflowOutputEvent, + WorkflowEvent, ) +from agent_framework.orchestrations import GroupChatBuilder from mem0 import AsyncMemory from pydantic import BaseModel, ValidationError -# from libs.agent_framework.manager_selection_response import ManagerSelectionResponse - logger = logging.getLogger(__name__) @@ -76,6 +73,17 @@ TOutput = TypeVar("TOutput", bound=BaseModel) # Output must be Pydantic model +class ManagerSelectionResponse(BaseModel): + """Coordinator selection payload parsed from JSON output.""" + + selected_participant: str | None = None + instruction: str | None = None + finish: bool | None = None + final_message: str | None = None + + model_config = {"extra": "allow"} + + @dataclass class AgentResponse: """Complete response captured from a single agent turn. @@ -150,7 +158,7 @@ class OrchestrationResult(Generic[TOutput]): """ success: bool - conversation: list[ChatMessage] + conversation: list[Message] agent_responses: list[AgentResponse] tool_usage: dict[str, list[dict[str, Any]]] result: TOutput | None = None @@ -255,8 +263,8 @@ def __init__( self, name: str, process_id: str, - participants: Mapping[str, AgentProtocol | Executor] - | Sequence[AgentProtocol | Executor], + participants: Mapping[str, SupportsAgentRun | Executor] + | Sequence[SupportsAgentRun | Executor], memory_client: AsyncMemory, coordinator_name: str = "Coordinator", max_rounds: int = 100, @@ -302,7 +310,7 @@ def __init__( self.result_format = result_output_format # Runtime state - self.agents: dict[str, ChatAgent] = participants + self.agents: dict[str, Agent] = participants self.agent_tool_usage: dict[str, list[dict[str, Any]]] = {} self.agent_responses: list[AgentResponse] = [] self._initialized: bool = False @@ -430,7 +438,7 @@ def get_result_generator_name(self) -> str: """ return "ResultGenerator" - def _validate_sign_offs(self, conversation: list[ChatMessage]) -> tuple[bool, str]: + def _validate_sign_offs(self, conversation: list[Message]) -> tuple[bool, str]: """Validate that all participating reviewers have ``SIGN-OFF: PASS``. This gate prevents the Coordinator from terminating with @@ -603,7 +611,7 @@ async def run_stream( self._tool_call_emitted.clear() self._tool_call_recorded.clear() self._tool_call_index.clear() - self._conversation: list[ChatMessage] = [] # Track conversation during workflow + self._conversation: list[Message] = [] # Track conversation during workflow try: # Ensure initialized @@ -617,9 +625,9 @@ async def run_stream( group_chat_workflow = await self._build_groupchat() # Execute with streaming - conversation: list[ChatMessage] = [] + conversation: list[Message] = [] - async for event in group_chat_workflow.run_stream(task_prompt): + async for event in group_chat_workflow.run(task_prompt, stream=True): # Enforce wall-clock timeout if configured. if self.max_seconds is not None: elapsed = (datetime.now() - start_time).total_seconds() @@ -631,7 +639,7 @@ async def run_stream( termination_type="hard_timeout", ) - if isinstance(event, AgentRunUpdateEvent): + if isinstance(event, AgentResponseUpdate): await self._handle_agent_update( event, stream_callback=on_agent_response_stream, @@ -653,7 +661,8 @@ async def run_stream( # If the Coordinator requested finish=true, stop immediately. if self._termination_requested: break - elif isinstance(event, WorkflowOutputEvent): + elif event.type == "output": + event: WorkflowEvent # Complete last agent's response before finishing if self._last_executor_id and self._current_agent_response: await self._complete_agent_response( @@ -770,7 +779,7 @@ async def run_stream( async def _handle_agent_update( self, - event: AgentRunUpdateEvent, + event: AgentResponseUpdate, stream_callback: AgentResponseStreamCallback | None = None, callback: AgentResponseCallback | None = None, ) -> None: @@ -838,7 +847,7 @@ async def _start_agent_if_needed( logger.info(f"\n[AGENT] {agent_name}:", extra={"agent_name": agent_name}) - def _append_text_chunk(self, event: AgentRunUpdateEvent) -> None: + def _append_text_chunk(self, event: AgentResponseUpdate) -> None: """Append streamed text chunks to the current agent buffer.""" if not hasattr(event.data, "text") or not event.data.text: return @@ -850,7 +859,7 @@ def _append_text_chunk(self, event: AgentRunUpdateEvent) -> None: async def _process_tool_calls( self, - event: AgentRunUpdateEvent, + event: AgentResponseUpdate, agent_name: str, stream_callback: AgentResponseStreamCallback | None, ) -> None: @@ -1017,7 +1026,7 @@ def _extract_function_calls(self, contents: Any) -> list[dict[str, Any]]: return calls def _backfill_tool_usage_from_conversation( - self, conversation: list[ChatMessage] + self, conversation: list[Message] ) -> None: """Populate ``agent_tool_usage`` from the final conversation messages. @@ -1032,7 +1041,7 @@ def _backfill_tool_usage_from_conversation( for msg in conversation: try: role = getattr(msg, "role", None) - if role != Role.ASSISTANT: + if role != "assistant": continue agent_name = getattr(msg, "author_name", None) or "assistant" @@ -1275,15 +1284,16 @@ async def _build_groupchat(self) -> Workflow: ] return ( - GroupChatBuilder() - .with_agent_orchestrator(agent=coordinator) - .participants(participants) + GroupChatBuilder( + orchestrator_agent=coordinator, + participants=participants, + ) .build() ) async def _generate_final_result( self, - conversation: list[ChatMessage], + conversation: list[Message], result_format: type[TOutput], result_generator_name: str, ) -> TOutput: @@ -1324,7 +1334,7 @@ async def _generate_final_result( result = await result_generator.run( final_conversation, - response_format=result_format, + options=ChatOptions(response_format=result_format), ) text = result.messages[-1].text @@ -1357,7 +1367,7 @@ async def _generate_final_result( ) retry_result = await result_generator.run( retry_conversation, - response_format=result_format, + options=ChatOptions(response_format=result_format), ) retry_text = retry_result.messages[-1].text retry_json_payload = self._extract_first_json_payload(retry_text) @@ -1403,7 +1413,7 @@ def _truncate_text( def _build_result_generator_conversation( self, - conversation: Iterable[ChatMessage], + conversation: Iterable[Message], *, exclude_authors: set[str] | None, max_messages: int, @@ -1411,7 +1421,7 @@ def _build_result_generator_conversation( max_chars_per_message: int, keep_head_chars: int, keep_tail_chars: int, - ) -> list[ChatMessage]: + ) -> list[Message]: """Build a size-bounded conversation slice for the ResultGenerator. The raw conversation can contain very large tool outputs or repeated @@ -1443,7 +1453,7 @@ def _build_result_generator_conversation( """ exclude = {a.lower() for a in (exclude_authors or set())} - selected: list[ChatMessage] = [] + selected: list[Message] = [] seen_fingerprints: set[tuple[str | None, str, str]] = set() total_chars = 0 @@ -1498,7 +1508,7 @@ def _build_result_generator_conversation( # Preserve role + author_name so downstream can attribute sign-offs. selected.append( - ChatMessage( + Message( role=role, text=truncated, author_name=author, diff --git a/src/ContentProcessorWorkflow/src/libs/agent_framework/middlewares.py b/src/ContentProcessorWorkflow/src/libs/agent_framework/middlewares.py index 274da362..f2df0063 100644 --- a/src/ContentProcessorWorkflow/src/libs/agent_framework/middlewares.py +++ b/src/ContentProcessorWorkflow/src/libs/agent_framework/middlewares.py @@ -33,14 +33,13 @@ from collections.abc import Awaitable, Callable from agent_framework import ( + AgentContext, AgentMiddleware, - AgentRunContext, ChatContext, - ChatMessage, ChatMiddleware, FunctionInvocationContext, FunctionMiddleware, - Role, + Message, ) logger = logging.getLogger(__name__) @@ -61,8 +60,8 @@ class DebuggingMiddleware(AgentMiddleware): async def process( self, - context: AgentRunContext, - next: Callable[[AgentRunContext], Awaitable[None]], + context: AgentContext, + next: Callable[[AgentContext], Awaitable[None]], ) -> None: """Print run diagnostics, inject debug flag, and delegate to next. @@ -241,18 +240,18 @@ async def process( for i, message in enumerate(context.messages): content = message.text if message.text else str(message.contents) - logger.debug(" Message %d (%s): %s", i + 1, message.role.value, content) + logger.debug(" Message %d (%s): %s", i + 1, message.role, content) logger.debug( "[InputObserverMiddleware] Total messages: %d", len(context.messages) ) # Modify user messages by creating new messages with enhanced text - modified_messages: list[ChatMessage] = [] + modified_messages: list[Message] = [] modified_count = 0 for message in context.messages: - if message.role == Role.USER and message.text: + if message.role == "user" and message.text: original_text = message.text updated_text = original_text @@ -264,7 +263,9 @@ async def process( updated_text, ) - modified_message = ChatMessage(role=message.role, text=updated_text) + modified_message = Message( + role=message.role, contents=[updated_text] + ) modified_messages.append(modified_message) modified_count += 1 else: diff --git a/src/ContentProcessorWorkflow/src/steps/claim_processor.py b/src/ContentProcessorWorkflow/src/steps/claim_processor.py index fa684153..420f1fab 100644 --- a/src/ContentProcessorWorkflow/src/steps/claim_processor.py +++ b/src/ContentProcessorWorkflow/src/steps/claim_processor.py @@ -34,14 +34,9 @@ from typing import Any from agent_framework import ( - ExecutorCompletedEvent, - ExecutorFailedEvent, - ExecutorInvokedEvent, Workflow, WorkflowBuilder, - WorkflowFailedEvent, - WorkflowOutputEvent, - WorkflowStartedEvent, + WorkflowEvent, ) from art import text2art @@ -155,45 +150,27 @@ def _init_workflow(self) -> Workflow: The built workflow ready to execute. """ + document_processing = DocumentProcessExecutor( + id="document_processing", app_context=self.app_context + ) + rai_analysis = RAIExecutor(id="rai_analysis", app_context=self.app_context) + summarizing = SummarizeExecutor(id="summarizing", app_context=self.app_context) + gap_analysis = GapExecutor(id="gap_analysis", app_context=self.app_context) + workflow = ( - WorkflowBuilder() - .register_executor( - lambda: DocumentProcessExecutor( - id="document_processing", app_context=self.app_context - ), - name="document_processing", - ) - .register_executor( - lambda: RAIExecutor(id="rai_analysis", app_context=self.app_context), - name="rai_analysis", - ) - .register_executor( - lambda: SummarizeExecutor( - id="summarizing", app_context=self.app_context - ), - name="summarizing", - ) - .register_executor( - lambda: GapExecutor(id="gap_analysis", app_context=self.app_context), - name="gap_analysis", - ) - .set_start_executor("document_processing") - # Edges define the execution flow and can include conditions for branching logic. - # In this case, we conditionally branch to the RAI analysis step based on the - # application configuration, allowing it to be toggled on/off without code changes. + WorkflowBuilder(start_executor=document_processing) .add_edge( - source="document_processing", - target="rai_analysis", + source=document_processing, + target=rai_analysis, condition=lambda _: self.app_context.configuration.app_rai_enabled, ) - .add_edge(source="rai_analysis", target="summarizing") - # If RAI analysis is disabled, the summarizing step will execute immediately after document processing + .add_edge(source=rai_analysis, target=summarizing) .add_edge( - source="document_processing", - target="summarizing", + source=document_processing, + target=summarizing, condition=lambda _: not self.app_context.configuration.app_rai_enabled, ) - .add_edge(source="summarizing", target="gap_analysis") + .add_edge(source=summarizing, target=gap_analysis) .build() ) @@ -233,10 +210,11 @@ async def run(self, input_data: str) -> Any: last_invoked_executor_id: str | None = None try: - async for event in self.workflow.run_stream(input_data): - if isinstance(event, WorkflowStartedEvent): + async for event in self.workflow.run(input_data, stream=True): + event: WorkflowEvent + if event.type == "started": logger.info("Workflow started (%s)", event.origin.value) - elif isinstance(event, WorkflowOutputEvent): + elif event.type == "output": claim_process_repository = self.app_context.get_service( Claim_Processes ) @@ -244,9 +222,9 @@ async def run(self, input_data: str) -> Any: process_id=input_data, new_status=Claim_Steps.COMPLETED ) return event.data - elif isinstance(event, ExecutorFailedEvent): + elif event.type == "executor_failed": last_failed_executor_id = event.executor_id - elif isinstance(event, WorkflowFailedEvent): + elif event.type == "failed": batch_id = input_data executor_id = ( event.details.executor_id @@ -266,7 +244,7 @@ async def run(self, input_data: str) -> Any: ) raise WorkflowExecutorFailedException(event.details) - elif isinstance(event, ExecutorInvokedEvent): + elif event.type == "executor_invoked": last_invoked_executor_id = event.executor_id logger.info("\n%s", text2art(event.executor_id.capitalize())) claim_process_repository = self.app_context.get_service( @@ -287,7 +265,7 @@ async def run(self, input_data: str) -> Any: await claim_process_repository.Update_Claim_Process_Status( process_id=input_data, new_status=new_status ) - elif isinstance(event, ExecutorCompletedEvent): + elif event.type == "executor_completed": pass else: pass diff --git a/src/ContentProcessorWorkflow/src/steps/document_process/executor/document_process_executor.py b/src/ContentProcessorWorkflow/src/steps/document_process/executor/document_process_executor.py index e9062375..f131c1a2 100644 --- a/src/ContentProcessorWorkflow/src/steps/document_process/executor/document_process_executor.py +++ b/src/ContentProcessorWorkflow/src/steps/document_process/executor/document_process_executor.py @@ -339,5 +339,5 @@ async def _on_poll(poll_data: dict) -> None: ) ) - await ctx.set_shared_state("workflow_output", workflow_output) + ctx.set_state("workflow_output", workflow_output) await ctx.send_message(workflow_output) diff --git a/src/ContentProcessorWorkflow/src/steps/gap_analysis/executor/gap_executor.py b/src/ContentProcessorWorkflow/src/steps/gap_analysis/executor/gap_executor.py index 5390b09b..f3714a61 100644 --- a/src/ContentProcessorWorkflow/src/steps/gap_analysis/executor/gap_executor.py +++ b/src/ContentProcessorWorkflow/src/steps/gap_analysis/executor/gap_executor.py @@ -16,9 +16,9 @@ import yaml from agent_framework import ( - ChatClientProtocol, - ChatMessage, Executor, + Message, + SupportsChatGetResponse, WorkflowContext, handler, ) @@ -142,7 +142,7 @@ async def handle_execute( ) ) - await ctx.set_shared_state("workflow_output", result) + ctx.set_state("workflow_output", result) await ctx.yield_output(result) return @@ -166,7 +166,7 @@ async def handle_execute( if agent_client is None: raise RuntimeError("Chat client 'default' is not configured.") - agent_client = cast(ChatClientProtocol, agent_client) + agent_client = cast(SupportsChatGetResponse, agent_client) claim_gap_analysis_prompt = self._load_prompt_and_rules() @@ -180,15 +180,17 @@ async def handle_execute( ) model_response = await agent.run( - ChatMessage( + Message( role="user", - text="Now analyze the following document extracts:\n\n" - + "\n\n".join( - [ - f"Document: {file.file_name} ({file.mime_type})\nExtracted Values with Schema (JSON):\n{file.extracted_content}" - for file in processed_files - ] - ), + contents=[ + "Now analyze the following document extracts:\n\n" + + "\n\n".join( + [ + f"Document: {file.file_name} ({file.mime_type})\nExtracted Values with Schema (JSON):\n{file.extracted_content}" + for file in processed_files + ] + ) + ], ) ) @@ -203,7 +205,7 @@ async def handle_execute( Executor_Output(step_name="gap_analysis", output_data=gap_result) ) - await ctx.set_shared_state("workflow_output", result) + ctx.set_state("workflow_output", result) await ctx.yield_output(result) async def fetch_processed_result(self, process_id: str) -> dict | None: diff --git a/src/ContentProcessorWorkflow/src/steps/rai/executor/rai_executor.py b/src/ContentProcessorWorkflow/src/steps/rai/executor/rai_executor.py index 64bca6f3..9b76efa0 100644 --- a/src/ContentProcessorWorkflow/src/steps/rai/executor/rai_executor.py +++ b/src/ContentProcessorWorkflow/src/steps/rai/executor/rai_executor.py @@ -12,9 +12,9 @@ from typing import cast from agent_framework import ( - ChatClientProtocol, - ChatMessage, Executor, + Message, + SupportsChatGetResponse, WorkflowContext, handler, ) @@ -118,7 +118,7 @@ async def handle_exectue( Executor_Output(step_name="rai_analysis", output_data=rai_result) ) - await ctx.set_shared_state("workflow_output", result) + ctx.set_state("workflow_output", result) await ctx.send_message(result) return @@ -161,7 +161,7 @@ async def handle_exectue( if agent_client is None: raise RuntimeError("Chat client 'default' is not configured.") - agent_client = cast(ChatClientProtocol, agent_client) + agent_client = cast(SupportsChatGetResponse, agent_client) rai_executor_prompt = self._load_rai_executor_prompt() @@ -180,9 +180,9 @@ async def handle_exectue( ) model_response = await agent.run( - ChatMessage( + Message( role="user", - text=document_text, + contents=[document_text], ) ) diff --git a/src/ContentProcessorWorkflow/src/steps/summarize/executor/summarize_executor.py b/src/ContentProcessorWorkflow/src/steps/summarize/executor/summarize_executor.py index a4e8b910..c3e84328 100644 --- a/src/ContentProcessorWorkflow/src/steps/summarize/executor/summarize_executor.py +++ b/src/ContentProcessorWorkflow/src/steps/summarize/executor/summarize_executor.py @@ -13,9 +13,9 @@ from typing import cast from agent_framework import ( - ChatClientProtocol, - ChatMessage, Executor, + Message, + SupportsChatGetResponse, WorkflowContext, handler, ) @@ -123,7 +123,7 @@ async def handle_execute( Executor_Output(step_name="summarizing", output_data=summarized_result) ) - await ctx.set_shared_state("workflow_output", result) + ctx.set_state("workflow_output", result) await ctx.send_message(result) return @@ -167,7 +167,7 @@ async def handle_execute( if agent_client is None: raise RuntimeError("Chat client 'default' is not configured.") - agent_client = cast(ChatClientProtocol, agent_client) + agent_client = cast(SupportsChatGetResponse, agent_client) claim_summarization_prompt = self._load_claim_summarization_prompt() @@ -181,14 +181,16 @@ async def handle_execute( ) model_response = await agent.run( - ChatMessage( + Message( role="user", - text="Now summarize the following document extracts: : \n\n".join( - [ - f"Document: {file.file_name}\nContent:\n{file.extracted_content}" - for file in processed_files - ] - ), + contents=[ + "Now summarize the following document extracts: : \n\n".join( + [ + f"Document: {file.file_name}\nContent:\n{file.extracted_content}" + for file in processed_files + ] + ) + ], ) ) @@ -202,7 +204,7 @@ async def handle_execute( Executor_Output(step_name="summarizing", output_data=summarized_result) ) - await ctx.set_shared_state("workflow_output", result) + ctx.set_state("workflow_output", result) await ctx.send_message(result) async def fetch_processed_steps_result(self, process_id: str) -> dict | None: diff --git a/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_agent_builder.py b/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_agent_builder.py index b17dd07b..89d7402b 100644 --- a/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_agent_builder.py +++ b/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_agent_builder.py @@ -67,7 +67,7 @@ def test_stores_all_attributes(self): assert builder._store is True assert builder._conversation_id == "conv-1" - @patch("libs.agent_framework.agent_builder.ChatAgent") + @patch("libs.agent_framework.agent_builder.Agent") def test_build_delegates_to_chat_agent(self, mock_chat_agent): client = _fake_chat_client() mock_chat_agent.return_value = "agent_instance" @@ -85,14 +85,14 @@ def test_build_delegates_to_chat_agent(self, mock_chat_agent): call_kwargs = mock_chat_agent.call_args assert call_kwargs.kwargs["name"] == "Bot" assert call_kwargs.kwargs["instructions"] == "Do stuff" - assert call_kwargs.kwargs["temperature"] == 0.5 + assert call_kwargs.kwargs["default_options"]["temperature"] == 0.5 # ── Static factory ─────────────────────────────────────────────────────────── class TestStaticFactory: - @patch("libs.agent_framework.agent_builder.ChatAgent") + @patch("libs.agent_framework.agent_builder.Agent") def test_create_agent_delegates_to_chat_agent(self, mock_chat_agent): client = _fake_chat_client() mock_chat_agent.return_value = "agent_instance" @@ -107,14 +107,14 @@ def test_create_agent_delegates_to_chat_agent(self, mock_chat_agent): assert agent == "agent_instance" call_kwargs = mock_chat_agent.call_args assert call_kwargs.kwargs["name"] == "Bot" - assert call_kwargs.kwargs["temperature"] == 0.3 + assert call_kwargs.kwargs["default_options"]["temperature"] == 0.3 # ── with_kwargs ────────────────────────────────────────────────────────────── class TestWithKwargs: - @patch("libs.agent_framework.agent_builder.ChatAgent") + @patch("libs.agent_framework.agent_builder.Agent") def test_extra_kwargs_forwarded(self, mock_chat_agent): client = _fake_chat_client() mock_chat_agent.return_value = "agent_instance" @@ -131,7 +131,7 @@ def test_extra_kwargs_forwarded(self, mock_chat_agent): class TestAdditionalChatOptions: def test_stores_options(self): client = _fake_chat_client() - opts = {"reasoning": {"effort": "high"}} + opts = {"reasoning_effort": "high"} builder = AgentBuilder(client).with_additional_chat_options(opts) assert builder._additional_chat_options == opts diff --git a/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_agent_framework_helper.py b/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_agent_framework_helper.py index 8e362fd7..bac746a9 100644 --- a/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_agent_framework_helper.py +++ b/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_agent_framework_helper.py @@ -95,7 +95,7 @@ def test_unsupported_type_raises_value_error(self): def test_azure_chat_completion_creates_client(self, mock_token): mock_token.return_value = lambda: "token" - with patch("agent_framework.azure.AzureOpenAIChatClient") as mock_cls: + with patch("agent_framework.openai.OpenAIChatCompletionClient") as mock_cls: mock_cls.return_value = "chat_client" client = AgentFrameworkHelper.create_client( client_type=ClientType.AzureOpenAIChatCompletion, @@ -108,7 +108,7 @@ def test_azure_chat_completion_creates_client(self, mock_token): def test_azure_response_creates_client(self, mock_token): mock_token.return_value = lambda: "token" - with patch("agent_framework.azure.AzureOpenAIResponsesClient") as mock_cls: + with patch("agent_framework.openai.OpenAIChatClient") as mock_cls: mock_cls.return_value = "response_client" client = AgentFrameworkHelper.create_client( client_type=ClientType.AzureOpenAIResponse, diff --git a/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_azure_openai_response_retry_utils.py b/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_azure_openai_response_retry_utils.py index ffd7838a..30d7ea79 100644 --- a/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_azure_openai_response_retry_utils.py +++ b/src/ContentProcessorWorkflow/tests/unit/libs/agent_framework/test_azure_openai_response_retry_utils.py @@ -5,7 +5,8 @@ """Unit tests for Azure OpenAI response retry utilities.""" import pytest -from agent_framework._types import ChatMessage, TextContent +from agent_framework import Message +from agent_framework._types import Content from libs.agent_framework.azure_openai_response_retry import ( ContextTrimConfig, @@ -107,11 +108,11 @@ def test_dict_message(self) -> None: assert _get_message_role({"role": "user", "content": "hi"}) == "user" def test_chatmessage_system(self) -> None: - m = ChatMessage(role="system", text="sys prompt") + m = Message(role="system", contents=["sys prompt"]) assert _get_message_role(m) == "system" def test_chatmessage_user(self) -> None: - m = ChatMessage(role="user", text="user msg") + m = Message(role="user", contents=["user msg"]) assert _get_message_role(m) == "user" def test_none_returns_none(self) -> None: @@ -125,12 +126,12 @@ def test_dict_content(self) -> None: assert _estimate_message_text({"content": "hello"}) == "hello" def test_chatmessage_text(self) -> None: - m = ChatMessage(role="user", text="hello world") + m = Message(role="user", contents=["hello world"]) assert _estimate_message_text(m) == "hello world" def test_chatmessage_large_text(self) -> None: big = "X" * 290_000 - m = ChatMessage(role="user", text=big) + m = Message(role="user", contents=[big]) assert len(_estimate_message_text(m)) == 290_000 @@ -143,11 +144,11 @@ def test_dict_message(self) -> None: assert result["content"] == "new" def test_chatmessage_replaces_contents(self) -> None: - m = ChatMessage(role="user", text="A" * 100_000) + m = Message(role="user", contents=["A" * 100_000]) result = _set_message_text(m, "truncated") assert result.text == "truncated" assert len(result.contents) == 1 - assert isinstance(result.contents[0], TextContent) + assert isinstance(result.contents[0], Content) class TestTrimMessagesWithChatMessage: @@ -174,8 +175,8 @@ def tight_cfg(self) -> ContextTrimConfig: def test_never_returns_empty_list(self, tight_cfg: ContextTrimConfig) -> None: """Core regression: _trim_messages must never return an empty list.""" messages = [ - ChatMessage(role="system", text="S" * 5_000), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["S" * 5_000]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=tight_cfg) assert len(result) >= 1, "trim must never drop all messages" @@ -183,8 +184,8 @@ def test_never_returns_empty_list(self, tight_cfg: ContextTrimConfig) -> None: def test_system_message_preserved(self, tight_cfg: ContextTrimConfig) -> None: """System message must be kept even when non-system messages are dropped.""" messages = [ - ChatMessage(role="system", text="System instructions"), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["System instructions"]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=tight_cfg) assert _get_message_role(result[0]) == "system" @@ -192,8 +193,8 @@ def test_system_message_preserved(self, tight_cfg: ContextTrimConfig) -> None: def test_truncation_respects_budget(self, tight_cfg: ContextTrimConfig) -> None: """After trimming, total chars must not exceed max_total_chars.""" messages = [ - ChatMessage(role="system", text="S" * 5_000), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["S" * 5_000]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=tight_cfg) total = sum(len(_estimate_message_text(m)) for m in result) @@ -201,7 +202,7 @@ def test_truncation_respects_budget(self, tight_cfg: ContextTrimConfig) -> None: def test_single_huge_message(self, tight_cfg: ContextTrimConfig) -> None: """A single message exceeding the budget is truncated, not dropped.""" - messages = [ChatMessage(role="user", text="X" * 500_000)] + messages = [Message(role="user", contents=["X" * 500_000])] result = _trim_messages(messages, cfg=tight_cfg) assert len(result) == 1 assert len(_estimate_message_text(result[0])) <= tight_cfg.max_total_chars @@ -219,8 +220,8 @@ def test_production_scenario_290k(self) -> None: retry_on_context_error=True, ) messages = [ - ChatMessage(role="system", text="S" * 5_607), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["S" * 5_607]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=cfg) assert len(result) >= 1, "must keep at least 1 message" @@ -231,8 +232,8 @@ def test_default_config_allows_290k(self) -> None: """With new defaults (800K budget), 290K input passes without trimming.""" cfg = ContextTrimConfig.from_env() messages = [ - ChatMessage(role="system", text="S" * 5_607), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["S" * 5_607]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=cfg) # 290K < 800K, so no trimming should occur; all messages kept intact. diff --git 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a/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_agent_builder.py b/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_agent_builder.py index b17dd07b..fc76abe3 100644 --- a/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_agent_builder.py +++ b/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_agent_builder.py @@ -67,7 +67,7 @@ def test_stores_all_attributes(self): assert builder._store is True assert builder._conversation_id == "conv-1" - @patch("libs.agent_framework.agent_builder.ChatAgent") + @patch("libs.agent_framework.agent_builder.Agent") def test_build_delegates_to_chat_agent(self, mock_chat_agent): client = _fake_chat_client() mock_chat_agent.return_value = "agent_instance" @@ -92,7 +92,7 @@ def test_build_delegates_to_chat_agent(self, mock_chat_agent): class TestStaticFactory: - @patch("libs.agent_framework.agent_builder.ChatAgent") + @patch("libs.agent_framework.agent_builder.Agent") def test_create_agent_delegates_to_chat_agent(self, mock_chat_agent): client = _fake_chat_client() mock_chat_agent.return_value = "agent_instance" @@ -114,7 +114,7 @@ def test_create_agent_delegates_to_chat_agent(self, mock_chat_agent): class TestWithKwargs: - @patch("libs.agent_framework.agent_builder.ChatAgent") + @patch("libs.agent_framework.agent_builder.Agent") def test_extra_kwargs_forwarded(self, mock_chat_agent): client = _fake_chat_client() mock_chat_agent.return_value = "agent_instance" @@ -131,7 +131,7 @@ def test_extra_kwargs_forwarded(self, mock_chat_agent): class TestAdditionalChatOptions: def test_stores_options(self): client = _fake_chat_client() - opts = {"reasoning": {"effort": "high"}} + opts = {"reasoning_effort": "high"} builder = AgentBuilder(client).with_additional_chat_options(opts) assert builder._additional_chat_options == opts diff --git a/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_agent_framework_helper.py b/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_agent_framework_helper.py index 876e57f6..cc3c1125 100644 --- a/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_agent_framework_helper.py +++ b/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_agent_framework_helper.py @@ -100,7 +100,7 @@ def test_azure_chat_completion_creates_client(self, mock_token): mock_token.return_value = lambda: "token" with patch( - "agent_framework.azure.AzureOpenAIChatClient" + "agent_framework.openai.OpenAIChatCompletionClient" ) as mock_cls: mock_cls.return_value = "chat_client" client = AgentFrameworkHelper.create_client( @@ -115,7 +115,7 @@ def test_azure_response_creates_client(self, mock_token): mock_token.return_value = lambda: "token" with patch( - "agent_framework.azure.AzureOpenAIResponsesClient" + "agent_framework.openai.OpenAIChatClient" ) as mock_cls: mock_cls.return_value = "response_client" client = AgentFrameworkHelper.create_client( diff --git a/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_azure_openai_response_retry_utils.py b/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_azure_openai_response_retry_utils.py index ffd7838a..0e78db31 100644 --- a/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_azure_openai_response_retry_utils.py +++ b/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_azure_openai_response_retry_utils.py @@ -5,7 +5,8 @@ """Unit tests for Azure OpenAI response retry utilities.""" import pytest -from agent_framework._types import ChatMessage, TextContent +from agent_framework import Message +from agent_framework._types import TextContent from libs.agent_framework.azure_openai_response_retry import ( ContextTrimConfig, @@ -107,11 +108,11 @@ def test_dict_message(self) -> None: assert _get_message_role({"role": "user", "content": "hi"}) == "user" def test_chatmessage_system(self) -> None: - m = ChatMessage(role="system", text="sys prompt") + m = Message(role="system", contents=["sys prompt"]) assert _get_message_role(m) == "system" def test_chatmessage_user(self) -> None: - m = ChatMessage(role="user", text="user msg") + m = Message(role="user", contents=["user msg"]) assert _get_message_role(m) == "user" def test_none_returns_none(self) -> None: @@ -125,12 +126,12 @@ def test_dict_content(self) -> None: assert _estimate_message_text({"content": "hello"}) == "hello" def test_chatmessage_text(self) -> None: - m = ChatMessage(role="user", text="hello world") + m = Message(role="user", contents=["hello world"]) assert _estimate_message_text(m) == "hello world" def test_chatmessage_large_text(self) -> None: big = "X" * 290_000 - m = ChatMessage(role="user", text=big) + m = Message(role="user", contents=[big]) assert len(_estimate_message_text(m)) == 290_000 @@ -143,7 +144,7 @@ def test_dict_message(self) -> None: assert result["content"] == "new" def test_chatmessage_replaces_contents(self) -> None: - m = ChatMessage(role="user", text="A" * 100_000) + m = Message(role="user", contents=["A" * 100_000]) result = _set_message_text(m, "truncated") assert result.text == "truncated" assert len(result.contents) == 1 @@ -174,8 +175,8 @@ def tight_cfg(self) -> ContextTrimConfig: def test_never_returns_empty_list(self, tight_cfg: ContextTrimConfig) -> None: """Core regression: _trim_messages must never return an empty list.""" messages = [ - ChatMessage(role="system", text="S" * 5_000), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["S" * 5_000]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=tight_cfg) assert len(result) >= 1, "trim must never drop all messages" @@ -183,8 +184,8 @@ def test_never_returns_empty_list(self, tight_cfg: ContextTrimConfig) -> None: def test_system_message_preserved(self, tight_cfg: ContextTrimConfig) -> None: """System message must be kept even when non-system messages are dropped.""" messages = [ - ChatMessage(role="system", text="System instructions"), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["System instructions"]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=tight_cfg) assert _get_message_role(result[0]) == "system" @@ -192,8 +193,8 @@ def test_system_message_preserved(self, tight_cfg: ContextTrimConfig) -> None: def test_truncation_respects_budget(self, tight_cfg: ContextTrimConfig) -> None: """After trimming, total chars must not exceed max_total_chars.""" messages = [ - ChatMessage(role="system", text="S" * 5_000), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["S" * 5_000]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=tight_cfg) total = sum(len(_estimate_message_text(m)) for m in result) @@ -201,7 +202,7 @@ def test_truncation_respects_budget(self, tight_cfg: ContextTrimConfig) -> None: def test_single_huge_message(self, tight_cfg: ContextTrimConfig) -> None: """A single message exceeding the budget is truncated, not dropped.""" - messages = [ChatMessage(role="user", text="X" * 500_000)] + messages = [Message(role="user", contents=["X" * 500_000])] result = _trim_messages(messages, cfg=tight_cfg) assert len(result) == 1 assert len(_estimate_message_text(result[0])) <= tight_cfg.max_total_chars @@ -219,8 +220,8 @@ def test_production_scenario_290k(self) -> None: retry_on_context_error=True, ) messages = [ - ChatMessage(role="system", text="S" * 5_607), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["S" * 5_607]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=cfg) assert len(result) >= 1, "must keep at least 1 message" @@ -231,8 +232,8 @@ def test_default_config_allows_290k(self) -> None: """With new defaults (800K budget), 290K input passes without trimming.""" cfg = ContextTrimConfig.from_env() messages = [ - ChatMessage(role="system", text="S" * 5_607), - ChatMessage(role="user", text="U" * 285_000), + Message(role="system", contents=["S" * 5_607]), + Message(role="user", contents=["U" * 285_000]), ] result = _trim_messages(messages, cfg=cfg) # 290K < 800K, so no trimming should occur; all messages kept intact. diff --git a/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_input_observer_middleware.py b/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_input_observer_middleware.py index 37d9bf5e..c03bb1ad 100644 --- a/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_input_observer_middleware.py +++ b/src/tests/ContentProcessorWorkflow/libs/agent_framework/test_input_observer_middleware.py @@ -7,7 +7,7 @@ import asyncio from types import SimpleNamespace -from agent_framework import ChatMessage, Role +from agent_framework import Message from libs.agent_framework.middlewares import InputObserverMiddleware @@ -16,7 +16,7 @@ def test_input_observer_middleware_replaces_user_text_when_configured() -> None: async def _run() -> None: ctx = SimpleNamespace( messages=[ - ChatMessage(role=Role.USER, text="original"), + Message(role="user", contents=["original"]), ] ) @@ -27,7 +27,7 @@ async def _next(_context): await mw.process(ctx, _next) - assert ctx.messages[0].role == Role.USER + assert ctx.messages[0].role == "user" assert ctx.messages[0].text == "replacement" asyncio.run(_run())