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1e350ea22f
* PR2: Wire context provider pipeline and update all internal consumers - Replace AgentThread with AgentSession across all packages - Replace ContextProvider with BaseContextProvider across all packages - Replace context_provider param with context_providers (Sequence) - Replace thread= with session= in run() signatures - Replace get_new_thread() with create_session() - Add get_session(service_session_id) to agent interface - DurableAgentThread -> DurableAgentSession - Remove _notify_thread_of_new_messages from WorkflowAgent - Wire before_run/after_run context provider pipeline in RawAgent - Auto-inject InMemoryHistoryProvider when no providers configured * fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files * refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider) * fix: update remaining ag-ui references (client docstring, getting_started sample) * fix: make get_session service_session_id keyword-only to avoid confusion with session_id * refactor: rename _RunContext.thread_messages to session_messages * refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists * rename: remove _new_ prefix from test files * refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider) * fix: read full history from session state directly instead of reaching into provider internals * fix: update stale .pyi stubs, sample imports, and README references for new provider types * fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples * refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider * refactor: UserInfoMemory stores state in session.state instead of instance attributes * feat: add Pydantic BaseModel support to session state serialization Pydantic models stored in session.state are now automatically serialized via model_dump() and restored via model_validate() during to_dict()/from_dict() round-trips. Models are auto-registered on first serialization; use register_state_type() for cold-start deserialization. Also export register_state_type as a public API. * fix mem0 * Update sample README links and descriptions for session terminology - Replace 'thread' with 'session' in sample descriptions across all READMEs - Update file links for renamed samples (mem0_sessions, redis_sessions, etc.) - Fix Threads section → Sessions section in main samples/README.md - Update tools, middleware, workflows, durabletask, azure_functions READMEs - Update architecture diagrams in concepts/tools/README.md - Update migration guides (autogen, semantic-kernel) * Fix broken Redis README link to renamed sample * Fix Mem0 OSS client search: pass scoping params as direct kwargs AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs, while AsyncMemoryClient (Platform) expects them in a filters dict. Adds tests for both client types. Port of fix from #3844 to new Mem0ContextProvider. * Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic - Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase - Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages - Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests - Add tests/workflow/__init__.py for relative import support - Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep) * Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection Chat clients that store history server-side by default (OpenAI Responses API, Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this during auto-injection and skips InMemoryHistoryProvider unless the user explicitly sets store=False. * Fix broken markdown links in azure_ai and redis READMEs * Fix getting-started samples to use session API instead of removed thread/ContextProvider API * updates to workflow as agent * fix group chat import * Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread - Fix: Propagate conversation_id from ChatResponse back to session.service_session_id in both streaming and non-streaming paths in _agents.py - Rename AgentThreadException → AgentSessionException - Remove stale AGUIThread from ag_ui lazy-loader - Rename use_service_thread → use_service_session in ag-ui package - Rename test functions from *_thread_* to *_session_* - Rename sample files from *_thread* to *_session* - Update docstrings and comments: thread → session - Update _mcp.py kwargs filter: add 'session' alongside 'thread' - Fix ContinuationToken docstring example: thread=thread → session=session - Fix _clients.py docstring: 'Agent threads' → 'Agent sessions' * Fix broken markdown links after thread→session file renames * fix azure ai test
180 lines
6.7 KiB
Python
180 lines
6.7 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from collections.abc import Awaitable, Callable
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from random import randint
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from typing import Annotated
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from agent_framework import (
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AgentContext,
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AgentMiddleware,
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AgentResponse,
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Message,
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MiddlewareTermination,
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tool,
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)
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from agent_framework.azure import AzureAIAgentClient
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from azure.identity.aio import AzureCliCredential
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from pydantic import Field
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"""
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MiddlewareTypes Termination Example
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This sample demonstrates how middleware can terminate execution using the `context.terminate` flag.
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The example includes:
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- PreTerminationMiddleware: Terminates execution before calling call_next() to prevent agent processing
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- PostTerminationMiddleware: Allows processing to complete but terminates further execution
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This is useful for implementing security checks, rate limiting, or early exit conditions.
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"""
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# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
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@tool(approval_mode="never_require")
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def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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) -> str:
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"""Get the weather for a given location."""
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conditions = ["sunny", "cloudy", "rainy", "stormy"]
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return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
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class PreTerminationMiddleware(AgentMiddleware):
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"""MiddlewareTypes that terminates execution before calling the agent."""
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def __init__(self, blocked_words: list[str]):
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self.blocked_words = [word.lower() for word in blocked_words]
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async def process(
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self,
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context: AgentContext,
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call_next: Callable[[], Awaitable[None]],
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) -> None:
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# Check if the user message contains any blocked words
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last_message = context.messages[-1] if context.messages else None
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if last_message and last_message.text:
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query = last_message.text.lower()
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for blocked_word in self.blocked_words:
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if blocked_word in query:
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print(f"[PreTerminationMiddleware] Blocked word '{blocked_word}' detected. Terminating request.")
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# Set a custom response
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context.result = AgentResponse(
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messages=[
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Message(
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role="assistant",
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text=(
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f"Sorry, I cannot process requests containing '{blocked_word}'. "
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"Please rephrase your question."
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),
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)
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]
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)
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# Terminate to prevent further processing
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raise MiddlewareTermination(result=context.result)
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await call_next()
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class PostTerminationMiddleware(AgentMiddleware):
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"""MiddlewareTypes that allows processing but terminates after reaching max responses across multiple runs."""
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def __init__(self, max_responses: int = 1):
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self.max_responses = max_responses
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self.response_count = 0
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async def process(
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self,
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context: AgentContext,
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call_next: Callable[[], Awaitable[None]],
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) -> None:
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print(f"[PostTerminationMiddleware] Processing request (response count: {self.response_count})")
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# Check if we should terminate before processing
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if self.response_count >= self.max_responses:
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print(
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f"[PostTerminationMiddleware] Maximum responses ({self.max_responses}) reached. "
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"Terminating further processing."
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)
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raise MiddlewareTermination
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# Allow the agent to process normally
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await call_next()
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# Increment response count after processing
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self.response_count += 1
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async def pre_termination_middleware() -> None:
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"""Demonstrate pre-termination middleware that blocks requests with certain words."""
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print("\n--- Example 1: Pre-termination MiddlewareTypes ---")
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentClient(credential=credential).as_agent(
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name="WeatherAgent",
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instructions="You are a helpful weather assistant.",
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tools=get_weather,
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middleware=[PreTerminationMiddleware(blocked_words=["bad", "inappropriate"])],
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) as agent,
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):
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# Test with normal query
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print("\n1. Normal query:")
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query = "What's the weather like in Seattle?"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result.text}")
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# Test with blocked word
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print("\n2. Query with blocked word:")
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query = "What's the bad weather in New York?"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result.text}")
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async def post_termination_middleware() -> None:
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"""Demonstrate post-termination middleware that limits responses across multiple runs."""
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print("\n--- Example 2: Post-termination MiddlewareTypes ---")
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentClient(credential=credential).as_agent(
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name="WeatherAgent",
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instructions="You are a helpful weather assistant.",
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tools=get_weather,
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middleware=[PostTerminationMiddleware(max_responses=1)],
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) as agent,
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):
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# First run (should work)
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print("\n1. First run:")
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query = "What's the weather in Paris?"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result.text}")
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# Second run (should be terminated by middleware)
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print("\n2. Second run (should be terminated):")
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query = "What about the weather in London?"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result.text if result and result.text else 'No response (terminated)'}")
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# Third run (should also be terminated)
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print("\n3. Third run (should also be terminated):")
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query = "And New York?"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result.text if result and result.text else 'No response (terminated)'}")
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async def main() -> None:
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"""Example demonstrating middleware termination functionality."""
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print("=== MiddlewareTypes Termination Example ===")
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await pre_termination_middleware()
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await post_termination_middleware()
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if __name__ == "__main__":
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asyncio.run(main())
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