chore: import upstream snapshot with attribution
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# Copyright (c) Microsoft. All rights reserved.
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from __future__ import annotations
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import asyncio
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from collections.abc import Awaitable, Mapping, Sequence
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from typing import Any
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from agent_framework import (
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GROUP_ANNOTATION_KEY,
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GROUP_TOKEN_COUNT_KEY,
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Agent,
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BaseChatClient,
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ChatResponse,
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Message,
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SlidingWindowStrategy,
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TruncationStrategy,
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)
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"""This sample demonstrates client defaults, agent overrides, and run-level overrides for in-run compaction.
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Key components:
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- A shared client with default `compaction_strategy` and `tokenizer`
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- An agent-level override that takes precedence over the shared client defaults
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- A run-level override passed through `agent.run(...)`
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"""
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class FixedTokenizer:
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"""Simple tokenizer used to make token annotations easy to inspect."""
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def __init__(self, token_count: int) -> None:
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self._token_count = token_count
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def count_tokens(self, text: str) -> int:
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return self._token_count
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class InspectingChatClient(BaseChatClient[Any]):
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"""Chat client that records the messages it receives after compaction."""
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def __init__(self, **kwargs: Any) -> None:
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super().__init__(**kwargs)
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self.last_messages: list[Message] = []
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def _inner_get_response(
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self,
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*,
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messages: Sequence[Message],
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stream: bool,
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options: Mapping[str, Any],
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**kwargs: Any,
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) -> Awaitable[ChatResponse]:
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if stream:
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raise ValueError("This sample only demonstrates non-streaming responses.")
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self.last_messages = list(messages)
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async def _get_response() -> ChatResponse:
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return ChatResponse(messages=[Message(role="assistant", contents=["done"])])
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return _get_response()
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def _build_messages() -> list[Message]:
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return [
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Message(role="user", contents=["Collect the deployment requirements."]),
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Message(role="assistant", contents=["I will gather the constraints first."]),
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Message(role="user", contents=["Summarize the rollout risks."]),
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Message(role="assistant", contents=["The main risks are drift, downtime, and rollback gaps."]),
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]
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def _token_count(message: Message) -> int | None:
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group_annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
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if not isinstance(group_annotation, dict):
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return None
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value = group_annotation.get(GROUP_TOKEN_COUNT_KEY)
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return value if isinstance(value, int) else None
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def _print_model_input(title: str, client: InspectingChatClient) -> None:
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print(f"\n{title}")
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print(f"Model receives {len(client.last_messages)} message(s):")
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for message in client.last_messages:
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print(f"- [{message.role}] {message.text} ({_token_count(message)} tokens)")
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async def main() -> None:
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# 1. Create one shared client with default compaction settings.
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shared_client = InspectingChatClient(
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compaction_strategy=TruncationStrategy(max_n=3, compact_to=2),
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tokenizer=FixedTokenizer(7),
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)
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# 2. Create one agent that relies on the client defaults.
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client_default_agent = Agent(client=shared_client, name="ClientDefaultAgent")
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# 3. Create another agent that overrides the shared client's defaults.
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agent_override = Agent(
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client=shared_client,
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name="AgentOverrideAgent",
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compaction_strategy=SlidingWindowStrategy(keep_last_groups=3),
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tokenizer=FixedTokenizer(11),
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)
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# 4. Run the first agent; the client defaults are applied.
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await client_default_agent.run(_build_messages())
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_print_model_input("1. Client default compaction", shared_client)
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# 5. Run the second agent; the agent-level override wins over the client defaults.
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await agent_override.run(_build_messages())
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_print_model_input("2. Agent-level override", shared_client)
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# 6. Override both settings for a single run; the per-run values win over both.
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await agent_override.run(
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_build_messages(),
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compaction_strategy=TruncationStrategy(max_n=2, compact_to=1),
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tokenizer=FixedTokenizer(23),
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)
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_print_model_input("3. Per-run override", shared_client)
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if __name__ == "__main__":
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asyncio.run(main())
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"""
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Sample output:
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1. Client default compaction
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Model receives 2 message(s):
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- [user] Summarize the rollout risks. (7 tokens)
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- [assistant] The main risks are drift, downtime, and rollback gaps. (7 tokens)
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2. Agent-level override
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Model receives 3 message(s):
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- [assistant] I will gather the constraints first. (11 tokens)
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- [user] Summarize the rollout risks. (11 tokens)
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- [assistant] The main risks are drift, downtime, and rollback gaps. (11 tokens)
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3. Per-run override
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Model receives 1 message(s):
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- [assistant] The main risks are drift, downtime, and rollback gaps. (23 tokens)
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"""
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