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feat: Support R1 reasoning text in model create result; enhance API docs (#5262)

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Resolves #5255 

---------

Co-authored-by: afourney <adamfo@microsoft.com>
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Eric Zhu 2025-01-30 11:03:54 -08:00 committed by GitHub
parent 44db2cc1fb
commit f656ff1e01
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12 changed files with 536 additions and 9 deletions
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3
python/packages/autogen-core/src/autogen_core/models/_model_client.py
View File

@ -22,9 +22,10 @@ class ModelFamily:
O1 = "o1"
GPT_4 = "gpt-4"
GPT_35 = "gpt-35"
R1 = "r1"
UNKNOWN = "unknown"
ANY: TypeAlias = Literal["gpt-4o", "o1", "gpt-4", "gpt-35", "unknown"]
ANY: TypeAlias = Literal["gpt-4o", "o1", "gpt-4", "gpt-35", "r1", "unknown"]
def __new__(cls, *args: Any, **kwargs: Any) -> ModelFamily:
raise TypeError(f"{cls.__name__} is a namespace class and cannot be instantiated.")

35
python/packages/autogen-core/src/autogen_core/models/_types.py
View File

@ -8,11 +8,25 @@ from .. import FunctionCall, Image
class SystemMessage(BaseModel):
"""System message contains instructions for the model coming from the developer.
.. note::
Open AI is moving away from using 'system' role in favor of 'developer' role.
See `Model Spec <https://cdn.openai.com/spec/model-spec-2024-05-08.html#definitions>`_ for more details.
However, the 'system' role is still allowed in their API and will be automatically converted to 'developer' role
on the server side.
So, you can use `SystemMessage` for developer messages.
"""
content: str
type: Literal["SystemMessage"] = "SystemMessage"
class UserMessage(BaseModel):
"""User message contains input from end users, or a catch-all for data provided to the model."""
content: Union[str, List[Union[str, Image]]]
# Name of the agent that sent this message
@ -22,6 +36,8 @@ class UserMessage(BaseModel):
class AssistantMessage(BaseModel):
"""Assistant message are sampled from the language model."""
content: Union[str, List[FunctionCall]]
# Name of the agent that sent this message
@ -31,11 +47,15 @@ class AssistantMessage(BaseModel):
class FunctionExecutionResult(BaseModel):
"""Function execution result contains the output of a function call."""
content: str
call_id: str
class FunctionExecutionResultMessage(BaseModel):
"""Function execution result message contains the output of multiple function calls."""
content: List[FunctionExecutionResult]
type: Literal["FunctionExecutionResultMessage"] = "FunctionExecutionResultMessage"
@ -69,8 +89,23 @@ class ChatCompletionTokenLogprob(BaseModel):
class CreateResult(BaseModel):
"""Create result contains the output of a model completion."""
finish_reason: FinishReasons
"""The reason the model finished generating the completion."""
content: Union[str, List[FunctionCall]]
"""The output of the model completion."""
usage: RequestUsage
"""The usage of tokens in the prompt and completion."""
cached: bool
"""Whether the completion was generated from a cached response."""
logprobs: Optional[List[ChatCompletionTokenLogprob] | None] = None
"""The logprobs of the tokens in the completion."""
thought: Optional[str] = None
"""The reasoning text for the completion if available. Used for reasoning models
and additional text content besides function calls."""

1
python/packages/autogen-ext/pyproject.toml
View File

@ -120,6 +120,7 @@ dev = [
"autogen_test_utils",
"langchain-experimental",
"pandas-stubs>=2.2.3.241126",
"httpx>=0.28.1",
]
[tool.ruff]

33
python/packages/autogen-ext/src/autogen_ext/models/_utils/parse_r1_content.py Normal file
View File

@ -0,0 +1,33 @@
import warnings
from typing import Tuple
def parse_r1_content(content: str) -> Tuple[str | None, str]:
"""Parse the content of an R1-style message that contains a `<think>...</think>` field."""
# Find the start and end of the think field
think_start = content.find("<think>")
think_end = content.find("</think>")
if think_start == -1 or think_end == -1:
warnings.warn(
"Could not find <think>..</think> field in model response content. " "No thought was extracted.",
UserWarning,
stacklevel=2,
)
return None, content
if think_end < think_start:
warnings.warn(
"Found </think> before <think> in model response content. " "No thought was extracted.",
UserWarning,
stacklevel=2,
)
return None, content
# Extract the think field
thought = content[think_start + len("<think>") : think_end].strip()
# Extract the rest of the content, skipping the think field.
content = content[think_end + len("</think>") :].strip()
return thought, content

15
python/packages/autogen-ext/src/autogen_ext/models/azure/_azure_ai_client.py
View File

@ -12,6 +12,7 @@ from autogen_core.models import (
FinishReasons,
FunctionExecutionResultMessage,
LLMMessage,
ModelFamily,
ModelInfo,
RequestUsage,
SystemMessage,
@ -55,6 +56,8 @@ from autogen_ext.models.azure.config import (
AzureAIChatCompletionClientConfig,
)
from .._utils.parse_r1_content import parse_r1_content
create_kwargs = set(getfullargspec(ChatCompletionsClient.complete).kwonlyargs)
AzureMessage = Union[AzureSystemMessage, AzureUserMessage, AzureAssistantMessage, AzureToolMessage]
@ -354,11 +357,17 @@ class AzureAIChatCompletionClient(ChatCompletionClient):
finish_reason = choice.finish_reason # type: ignore
content = choice.message.content or ""
if isinstance(content, str) and self._model_info["family"] == ModelFamily.R1:
thought, content = parse_r1_content(content)
else:
thought = None
response = CreateResult(
finish_reason=finish_reason, # type: ignore
content=content,
usage=usage,
cached=False,
thought=thought,
)
self.add_usage(usage)
@ -464,11 +473,17 @@ class AzureAIChatCompletionClient(ChatCompletionClient):
prompt_tokens=prompt_tokens,
)
if isinstance(content, str) and self._model_info["family"] == ModelFamily.R1:
thought, content = parse_r1_content(content)
else:
thought = None
result = CreateResult(
finish_reason=finish_reason,
content=content,
usage=usage,
cached=False,
thought=thought,
)
self.add_usage(usage)

31
python/packages/autogen-ext/src/autogen_ext/models/openai/_openai_client.py
View File

@ -72,6 +72,7 @@ from openai.types.shared_params import FunctionDefinition, FunctionParameters
from pydantic import BaseModel
from typing_extensions import Self, Unpack
from .._utils.parse_r1_content import parse_r1_content
from . import _model_info
from .config import (
AzureOpenAIClientConfiguration,
@ -605,12 +606,19 @@ class BaseOpenAIChatCompletionClient(ChatCompletionClient):
)
for x in choice.logprobs.content
]
if isinstance(content, str) and self._model_info["family"] == ModelFamily.R1:
thought, content = parse_r1_content(content)
else:
thought = None
response = CreateResult(
finish_reason=normalize_stop_reason(finish_reason),
content=content,
usage=usage,
cached=False,
logprobs=logprobs,
thought=thought,
)
self._total_usage = _add_usage(self._total_usage, usage)
@ -818,12 +826,18 @@ class BaseOpenAIChatCompletionClient(ChatCompletionClient):
completion_tokens=completion_tokens,
)
if isinstance(content, str) and self._model_info["family"] == ModelFamily.R1:
thought, content = parse_r1_content(content)
else:
thought = None
result = CreateResult(
finish_reason=normalize_stop_reason(stop_reason),
content=content,
usage=usage,
cached=False,
logprobs=logprobs,
thought=thought,
)
self._total_usage = _add_usage(self._total_usage, usage)
@ -992,20 +1006,23 @@ class OpenAIChatCompletionClient(BaseOpenAIChatCompletionClient, Component[OpenA
print(result)
To use the client with a non-OpenAI model, you need to provide the base URL of the model and the model capabilities:
To use the client with a non-OpenAI model, you need to provide the base URL of the model and the model info.
For example, to use Ollama, you can use the following code snippet:
.. code-block:: python
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
custom_model_client = OpenAIChatCompletionClient(
model="custom-model-name",
base_url="https://custom-model.com/reset/of/the/path",
model="deepseek-r1:1.5b",
base_url="http://localhost:11434/v1",
api_key="placeholder",
model_capabilities={
"vision": True,
"function_calling": True,
"json_output": True,
model_info={
"vision": False,
"function_calling": False,
"json_output": False,
"family": ModelFamily.R1,
},
)

15
python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_sk_chat_completion_adapter.py
View File

@ -25,6 +25,8 @@ from typing_extensions import AsyncGenerator, Union
from autogen_ext.tools.semantic_kernel import KernelFunctionFromTool
from .._utils.parse_r1_content import parse_r1_content
class SKChatCompletionAdapter(ChatCompletionClient):
"""
@ -402,11 +404,17 @@ class SKChatCompletionAdapter(ChatCompletionClient):
content = result[0].content
finish_reason = "stop"
if isinstance(content, str) and self._model_info["family"] == ModelFamily.R1:
thought, content = parse_r1_content(content)
else:
thought = None
return CreateResult(
content=content,
finish_reason=finish_reason,
usage=RequestUsage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens),
cached=False,
thought=thought,
)
async def create_stream(
@ -485,11 +493,18 @@ class SKChatCompletionAdapter(ChatCompletionClient):
if accumulated_content:
self._total_prompt_tokens += prompt_tokens
self._total_completion_tokens += completion_tokens
if isinstance(accumulated_content, str) and self._model_info["family"] == ModelFamily.R1:
thought, accumulated_content = parse_r1_content(accumulated_content)
else:
thought = None
yield CreateResult(
content=accumulated_content,
finish_reason="stop",
usage=RequestUsage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens),
cached=False,
thought=thought,
)
def actual_usage(self) -> RequestUsage:

81
python/packages/autogen-ext/tests/models/test_azure_ai_model_client.py
View File

@ -5,7 +5,7 @@ from typing import Any, AsyncGenerator, List
import pytest
from autogen_core import CancellationToken, FunctionCall, Image
from autogen_core.models import CreateResult, UserMessage
from autogen_core.models import CreateResult, ModelFamily, UserMessage
from autogen_ext.models.azure import AzureAIChatCompletionClient
from azure.ai.inference.aio import (
ChatCompletionsClient,
@ -295,3 +295,82 @@ async def test_multimodal_supported(monkeypatch: pytest.MonkeyPatch) -> None:
]
)
assert result.content == "Handled image"
@pytest.mark.asyncio
async def test_r1_content(monkeypatch: pytest.MonkeyPatch) -> None:
"""
Ensures that the content is parsed correctly when it contains an R1-style think field.
"""
async def _mock_create_r1_content_stream(
*args: Any, **kwargs: Any
) -> AsyncGenerator[StreamingChatCompletionsUpdate, None]:
mock_chunks_content = ["<think>Thought</think> Hello", " Another Hello", " Yet Another Hello"]
mock_chunks = [
StreamingChatChoiceUpdate(
index=0,
finish_reason="stop",
delta=StreamingChatResponseMessageUpdate(role="assistant", content=chunk_content),
)
for chunk_content in mock_chunks_content
]
for mock_chunk in mock_chunks:
await asyncio.sleep(0.1)
yield StreamingChatCompletionsUpdate(
id="id",
choices=[mock_chunk],
created=datetime.now(),
model="model",
usage=CompletionsUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
)
async def _mock_create_r1_content(
*args: Any, **kwargs: Any
) -> ChatCompletions | AsyncGenerator[StreamingChatCompletionsUpdate, None]:
stream = kwargs.get("stream", False)
if not stream:
await asyncio.sleep(0.1)
return ChatCompletions(
id="id",
created=datetime.now(),
model="model",
choices=[
ChatChoice(
index=0,
finish_reason="stop",
message=ChatResponseMessage(content="<think>Thought</think> Hello", role="assistant"),
)
],
usage=CompletionsUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
)
else:
return _mock_create_r1_content_stream(*args, **kwargs)
monkeypatch.setattr(ChatCompletionsClient, "complete", _mock_create_r1_content)
client = AzureAIChatCompletionClient(
endpoint="endpoint",
credential=AzureKeyCredential("api_key"),
model_info={
"json_output": False,
"function_calling": False,
"vision": True,
"family": ModelFamily.R1,
},
model="model",
)
result = await client.create(messages=[UserMessage(content="Hello", source="user")])
assert result.content == "Hello"
assert result.thought == "Thought"
chunks: List[str | CreateResult] = []
async for chunk in client.create_stream(messages=[UserMessage(content="Hello", source="user")]):
chunks.append(chunk)
assert isinstance(chunks[-1], CreateResult)
assert chunks[-1].content == "Hello Another Hello Yet Another Hello"
assert chunks[-1].thought == "Thought"

214
python/packages/autogen-ext/tests/models/test_openai_model_client.py
View File

@ -4,6 +4,7 @@ import os
from typing import Annotated, Any, AsyncGenerator, Dict, Generic, List, Literal, Tuple, TypeVar
from unittest.mock import MagicMock
import httpx
import pytest
from autogen_core import CancellationToken, FunctionCall, Image
from autogen_core.models import (
@ -12,6 +13,7 @@ from autogen_core.models import (
FunctionExecutionResult,
FunctionExecutionResultMessage,
LLMMessage,
ModelInfo,
RequestUsage,
SystemMessage,
UserMessage,
@ -468,6 +470,154 @@ async def test_structured_output(monkeypatch: pytest.MonkeyPatch) -> None:
assert response.response == "happy"
@pytest.mark.asyncio
async def test_r1_think_field(monkeypatch: pytest.MonkeyPatch) -> None:
async def _mock_create_stream(*args: Any, **kwargs: Any) -> AsyncGenerator[ChatCompletionChunk, None]:
chunks = ["<think> Hello</think>", " Another Hello", " Yet Another Hello"]
for i, chunk in enumerate(chunks):
await asyncio.sleep(0.1)
yield ChatCompletionChunk(
id="id",
choices=[
ChunkChoice(
finish_reason="stop" if i == len(chunks) - 1 else None,
index=0,
delta=ChoiceDelta(
content=chunk,
role="assistant",
),
),
],
created=0,
model="r1",
object="chat.completion.chunk",
usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
)
async def _mock_create(*args: Any, **kwargs: Any) -> ChatCompletion | AsyncGenerator[ChatCompletionChunk, None]:
stream = kwargs.get("stream", False)
if not stream:
await asyncio.sleep(0.1)
return ChatCompletion(
id="id",
choices=[
Choice(
finish_reason="stop",
index=0,
message=ChatCompletionMessage(
content="<think> Hello</think> Another Hello Yet Another Hello", role="assistant"
),
)
],
created=0,
model="r1",
object="chat.completion",
usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
)
else:
return _mock_create_stream(*args, **kwargs)
monkeypatch.setattr(AsyncCompletions, "create", _mock_create)
model_client = OpenAIChatCompletionClient(
model="r1",
api_key="",
model_info={"family": ModelFamily.R1, "vision": False, "function_calling": False, "json_output": False},
)
# Successful completion with think field.
create_result = await model_client.create(messages=[UserMessage(content="I am happy.", source="user")])
assert create_result.content == "Another Hello Yet Another Hello"
assert create_result.finish_reason == "stop"
assert not create_result.cached
assert create_result.thought == "Hello"
# Stream completion with think field.
chunks: List[str | CreateResult] = []
async for chunk in model_client.create_stream(messages=[UserMessage(content="Hello", source="user")]):
chunks.append(chunk)
assert len(chunks) > 0
assert isinstance(chunks[-1], CreateResult)
assert chunks[-1].content == "Another Hello Yet Another Hello"
assert chunks[-1].thought == "Hello"
assert not chunks[-1].cached
@pytest.mark.asyncio
async def test_r1_think_field_not_present(monkeypatch: pytest.MonkeyPatch) -> None:
async def _mock_create_stream(*args: Any, **kwargs: Any) -> AsyncGenerator[ChatCompletionChunk, None]:
chunks = ["Hello", " Another Hello", " Yet Another Hello"]
for i, chunk in enumerate(chunks):
await asyncio.sleep(0.1)
yield ChatCompletionChunk(
id="id",
choices=[
ChunkChoice(
finish_reason="stop" if i == len(chunks) - 1 else None,
index=0,
delta=ChoiceDelta(
content=chunk,
role="assistant",
),
),
],
created=0,
model="r1",
object="chat.completion.chunk",
usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
)
async def _mock_create(*args: Any, **kwargs: Any) -> ChatCompletion | AsyncGenerator[ChatCompletionChunk, None]:
stream = kwargs.get("stream", False)
if not stream:
await asyncio.sleep(0.1)
return ChatCompletion(
id="id",
choices=[
Choice(
finish_reason="stop",
index=0,
message=ChatCompletionMessage(
content="Hello Another Hello Yet Another Hello", role="assistant"
),
)
],
created=0,
model="r1",
object="chat.completion",
usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
)
else:
return _mock_create_stream(*args, **kwargs)
monkeypatch.setattr(AsyncCompletions, "create", _mock_create)
model_client = OpenAIChatCompletionClient(
model="r1",
api_key="",
model_info={"family": ModelFamily.R1, "vision": False, "function_calling": False, "json_output": False},
)
# Warning completion when think field is not present.
with pytest.warns(UserWarning, match="Could not find <think>..</think> field in model response content."):
create_result = await model_client.create(messages=[UserMessage(content="I am happy.", source="user")])
assert create_result.content == "Hello Another Hello Yet Another Hello"
assert create_result.finish_reason == "stop"
assert not create_result.cached
assert create_result.thought is None
# Stream completion with think field.
with pytest.warns(UserWarning, match="Could not find <think>..</think> field in model response content."):
chunks: List[str | CreateResult] = []
async for chunk in model_client.create_stream(messages=[UserMessage(content="Hello", source="user")]):
chunks.append(chunk)
assert len(chunks) > 0
assert isinstance(chunks[-1], CreateResult)
assert chunks[-1].content == "Hello Another Hello Yet Another Hello"
assert chunks[-1].thought is None
assert not chunks[-1].cached
@pytest.mark.asyncio
async def test_tool_calling(monkeypatch: pytest.MonkeyPatch) -> None:
model = "gpt-4o-2024-05-13"
@ -836,4 +986,68 @@ async def test_hugging_face() -> None:
await _test_model_client_basic_completion(model_client)
@pytest.mark.asyncio
async def test_ollama() -> None:
model = "deepseek-r1:1.5b"
model_info: ModelInfo = {
"function_calling": False,
"json_output": False,
"vision": False,
"family": ModelFamily.R1,
}
# Check if the model is running locally.
try:
async with httpx.AsyncClient() as client:
response = await client.get(f"http://localhost:11434/v1/models/{model}")
response.raise_for_status()
except httpx.HTTPStatusError as e:
pytest.skip(f"{model} model is not running locally: {e}")
except httpx.ConnectError as e:
pytest.skip(f"Ollama is not running locally: {e}")
model_client = OpenAIChatCompletionClient(
model=model,
api_key="placeholder",
base_url="http://localhost:11434/v1",
model_info=model_info,
)
# Test basic completion with the Ollama deepseek-r1:1.5b model.
create_result = await model_client.create(
messages=[
UserMessage(
content="Taking two balls from a bag of 10 green balls and 20 red balls, "
"what is the probability of getting a green and a red balls?",
source="user",
),
]
)
assert isinstance(create_result.content, str)
assert len(create_result.content) > 0
assert create_result.finish_reason == "stop"
assert create_result.usage is not None
if model_info["family"] == ModelFamily.R1:
assert create_result.thought is not None
# Test streaming completion with the Ollama deepseek-r1:1.5b model.
chunks: List[str | CreateResult] = []
async for chunk in model_client.create_stream(
messages=[
UserMessage(
content="Taking two balls from a bag of 10 green balls and 20 red balls, "
"what is the probability of getting a green and a red balls?",
source="user",
),
]
):
chunks.append(chunk)
assert len(chunks) > 0
assert isinstance(chunks[-1], CreateResult)
assert chunks[-1].finish_reason == "stop"
assert len(chunks[-1].content) > 0
assert chunks[-1].usage is not None
if model_info["family"] == ModelFamily.R1:
assert chunks[-1].thought is not None
# TODO: add integration tests for Azure OpenAI using AAD token.

72
python/packages/autogen-ext/tests/models/test_sk_chat_completion_adapter.py
View File

@ -377,3 +377,75 @@ async def test_sk_chat_completion_custom_model_info(sk_client: AzureChatCompleti
# Verify capabilities returns the same ModelInfo
assert adapter.capabilities == adapter.model_info
@pytest.mark.asyncio
async def test_sk_chat_completion_r1_content() -> None:
async def mock_get_chat_message_contents(
chat_history: ChatHistory,
settings: PromptExecutionSettings,
**kwargs: Any,
) -> list[ChatMessageContent]:
return [
ChatMessageContent(
ai_model_id="r1",
role=AuthorRole.ASSISTANT,
metadata={"usage": {"prompt_tokens": 20, "completion_tokens": 9}},
items=[TextContent(text="<think>Reasoning...</think> Hello!")],
finish_reason=FinishReason.STOP,
)
]
async def mock_get_streaming_chat_message_contents(
chat_history: ChatHistory,
settings: PromptExecutionSettings,
**kwargs: Any,
) -> AsyncGenerator[list["StreamingChatMessageContent"], Any]:
chunks = ["<think>Reasoning...</think>", " Hello!"]
for i, chunk in enumerate(chunks):
yield [
StreamingChatMessageContent(
choice_index=0,
inner_content=ChatCompletionChunk(
id=f"chatcmpl-{i}",
choices=[Choice(delta=ChoiceDelta(content=chunk), finish_reason=None, index=0)],
created=1736674044,
model="r1",
object="chat.completion.chunk",
service_tier="scale",
system_fingerprint="fingerprint",
usage=CompletionUsage(prompt_tokens=20, completion_tokens=9, total_tokens=29),
),
ai_model_id="gpt-4o-mini",
metadata={"id": f"chatcmpl-{i}", "created": 1736674044},
role=AuthorRole.ASSISTANT,
items=[StreamingTextContent(choice_index=0, text=chunk)],
finish_reason=FinishReason.STOP if i == len(chunks) - 1 else None,
)
]
mock_client = AsyncMock(spec=AzureChatCompletion)
mock_client.get_chat_message_contents = mock_get_chat_message_contents
mock_client.get_streaming_chat_message_contents = mock_get_streaming_chat_message_contents
kernel = Kernel(memory=NullMemory())
adapter = SKChatCompletionAdapter(
mock_client,
kernel=kernel,
model_info=ModelInfo(vision=False, function_calling=False, json_output=False, family=ModelFamily.R1),
)
result = await adapter.create(messages=[UserMessage(content="Say hello!", source="user")])
assert result.finish_reason == "stop"
assert result.content == "Hello!"
assert result.thought == "Reasoning..."
response_chunks: list[CreateResult | str] = []
async for chunk in adapter.create_stream(messages=[UserMessage(content="Say hello!", source="user")]):
response_chunks.append(chunk)
assert len(response_chunks) > 0
assert isinstance(response_chunks[-1], CreateResult)
assert response_chunks[-1].finish_reason == "stop"
assert response_chunks[-1].content == "Hello!"
assert response_chunks[-1].thought == "Reasoning..."

43
python/packages/autogen-ext/tests/models/test_utils.py Normal file
View File

@ -0,0 +1,43 @@
import pytest
from autogen_ext.models._utils.parse_r1_content import parse_r1_content
def test_parse_r1_content() -> None:
content = "Hello, <think>world</think> How are you?"
thought, content = parse_r1_content(content)
assert thought == "world"
assert content == "How are you?"
with pytest.warns(
UserWarning,
match="Could not find <think>..</think> field in model response content. " "No thought was extracted.",
):
content = "Hello, world How are you?"
thought, content = parse_r1_content(content)
assert thought is None
assert content == "Hello, world How are you?"
with pytest.warns(
UserWarning,
match="Could not find <think>..</think> field in model response content. " "No thought was extracted.",
):
content = "Hello, <think>world How are you?"
thought, content = parse_r1_content(content)
assert thought is None
assert content == "Hello, <think>world How are you?"
with pytest.warns(
UserWarning, match="Found </think> before <think> in model response content. " "No thought was extracted."
):
content = "</think>Hello, <think>world</think>"
thought, content = parse_r1_content(content)
assert thought is None
assert content == "</think>Hello, <think>world</think>"
with pytest.warns(
UserWarning, match="Found </think> before <think> in model response content. " "No thought was extracted."
):
content = "</think>Hello, <think>world"
thought, content = parse_r1_content(content)
assert thought is None
assert content == "</think>Hello, <think>world"

2
python/uv.lock generated
View File

@ -654,6 +654,7 @@ web-surfer = [
[package.dev-dependencies]
dev = [
{ name = "autogen-test-utils" },
{ name = "httpx" },
{ name = "langchain-experimental" },
{ name = "pandas-stubs" },
]
@ -706,6 +707,7 @@ requires-dist = [
[package.metadata.requires-dev]
dev = [
{ name = "autogen-test-utils", editable = "packages/autogen-test-utils" },
{ name = "httpx", specifier = ">=0.28.1" },
{ name = "langchain-experimental" },
{ name = "pandas-stubs", specifier = ">=2.2.3.241126" },
]