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feat(anthropic/chat/transformation.py): add minimal reasoning effort thinking support

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Closes Slack issue
This commit is contained in:
Krrish Dholakia 2025-11-12 13:57:07 -08:00
parent 2843dab7fe
commit 48579f7539
1 changed files with 28 additions and 27 deletions
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55
litellm/llms/anthropic/chat/transformation.py
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@ -12,6 +12,7 @@ from litellm.constants import (
DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET,
RESPONSE_FORMAT_TOOL_NAME,
)
from litellm.litellm_core_utils.core_helpers import map_finish_reason
@ -52,10 +53,7 @@ from litellm.types.utils import (
CompletionTokensDetailsWrapper,
)
from litellm.types.utils import Message as LitellmMessage
from litellm.types.utils import (
PromptTokensDetailsWrapper,
ServerToolUse,
)
from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse
from litellm.utils import (
ModelResponse,
Usage,
@ -82,9 +80,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
"""
max_tokens: Optional[
int
] = DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS # anthropic requires a default value (Opus, Sonnet, and Haiku have the same default)
max_tokens: Optional[int] = (
DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS # anthropic requires a default value (Opus, Sonnet, and Haiku have the same default)
)
stop_sequences: Optional[list] = None
temperature: Optional[int] = None
top_p: Optional[int] = None
@ -378,6 +376,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
type="enabled",
budget_tokens=DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
)
elif reasoning_effort == "minimal":
return AnthropicThinkingParam(
type="enabled",
budget_tokens=DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET,
)
else:
raise ValueError(f"Unmapped reasoning effort: {reasoning_effort}")
@ -464,11 +467,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
if mcp_servers:
optional_params["mcp_servers"] = mcp_servers
if param == "tool_choice" or param == "parallel_tool_calls":
_tool_choice: Optional[
AnthropicMessagesToolChoice
] = self._map_tool_choice(
tool_choice=non_default_params.get("tool_choice"),
parallel_tool_use=non_default_params.get("parallel_tool_calls"),
_tool_choice: Optional[AnthropicMessagesToolChoice] = (
self._map_tool_choice(
tool_choice=non_default_params.get("tool_choice"),
parallel_tool_use=non_default_params.get("parallel_tool_calls"),
)
)
if _tool_choice is not None:
@ -576,9 +579,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
text=system_message_block["content"],
)
if "cache_control" in system_message_block:
anthropic_system_message_content[
"cache_control"
] = system_message_block["cache_control"]
anthropic_system_message_content["cache_control"] = (
system_message_block["cache_control"]
)
anthropic_system_message_list.append(
anthropic_system_message_content
)
@ -592,9 +595,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
)
)
if "cache_control" in _content:
anthropic_system_message_content[
"cache_control"
] = _content["cache_control"]
anthropic_system_message_content["cache_control"] = (
_content["cache_control"]
)
anthropic_system_message_list.append(
anthropic_system_message_content
@ -652,15 +655,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
if tool.get("type", None) and tool.get("type").startswith(
ANTHROPIC_HOSTED_TOOLS.WEB_FETCH.value
):
headers[
"anthropic-beta"
] = ANTHROPIC_BETA_HEADER_VALUES.WEB_FETCH_2025_09_10.value
headers["anthropic-beta"] = (
ANTHROPIC_BETA_HEADER_VALUES.WEB_FETCH_2025_09_10.value
)
elif tool.get("type", None) and tool.get("type").startswith(
ANTHROPIC_HOSTED_TOOLS.MEMORY.value
):
headers[
"anthropic-beta"
] = ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
headers["anthropic-beta"] = (
ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
)
return headers
def transform_request(
@ -779,9 +782,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
)
return _message
def extract_response_content(
self, completion_response: dict
) -> Tuple[
def extract_response_content(self, completion_response: dict) -> Tuple[
str,
Optional[List[Any]],
Optional[