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Add provider specific headers in their files

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This commit is contained in:
Sameer Kankute 2025-11-27 21:25:45 +05:30
parent c7ef668d78
commit c12305ac3c
4 changed files with 50 additions and 64 deletions
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28
litellm/llms/anthropic/common_utils.py
View File

@ -203,8 +203,6 @@ class AnthropicModelInfo(BaseLLMModelInfo):
def get_anthropic_beta_list(
self,
model: str,
custom_llm_provider: str,
tools: Optional[List] = None,
optional_params: Optional[dict] = None,
computer_tool_used: Optional[str] = None,
prompt_caching_set: bool = False,
@ -212,44 +210,20 @@ class AnthropicModelInfo(BaseLLMModelInfo):
mcp_server_used: bool = False,
) -> List[str]:
"""
Get list of beta headers based on provider and features used.
This method provides provider-specific beta header values for different Anthropic features.
Different providers (Anthropic API, Bedrock, VertexAI, Microsoft Foundry) may require
different beta header values for the same feature.
Get list of common beta headers based on the features that are active.
Returns:
List of beta header strings
"""
from litellm.types.llms.anthropic import (
ANTHROPIC_EFFORT_BETA_HEADER,
ANTHROPIC_TOOL_SEARCH_BETA_HEADER,
)
betas = []
# Detect features
tool_search_used = self.is_tool_search_used(tools)
programmatic_tool_calling_used = self.is_programmatic_tool_calling_used(tools)
input_examples_used = self.is_input_examples_used(tools)
effort_used = self.is_effort_used(optional_params, model)
# Add beta headers based on provider
if custom_llm_provider in ["vertex_ai", "vertex_ai_beta"]:
if tool_search_used:
betas.append("tool-search-tool-2025-10-19")
# VertexAI doesn't support programmatic tool calling or input_examples yet
elif custom_llm_provider == "bedrock":
# Bedrock: tool-search only for Opus 4.5, advanced-tool-use for programmatic/input_examples
if tool_search_used and ("opus-4" in model.lower() or "opus_4" in model.lower()):
betas.append("tool-search-tool-2025-10-19")
if programmatic_tool_calling_used or input_examples_used:
betas.append(ANTHROPIC_TOOL_SEARCH_BETA_HEADER) # advanced-tool-use-2025-11-20
else: # anthropic, azure (Microsoft Foundry), and others
# Direct API and Microsoft Foundry use advanced-tool-use for all
if tool_search_used or programmatic_tool_calling_used or input_examples_used:
betas.append(ANTHROPIC_TOOL_SEARCH_BETA_HEADER) # advanced-tool-use-2025-11-20
if effort_used:
betas.append(ANTHROPIC_EFFORT_BETA_HEADER) # effort-2025-11-24

31
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
View File

@ -7,6 +7,7 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation
AmazonInvokeConfig,
)
from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers
from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse
@ -92,28 +93,32 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
if "anthropic_version" not in _anthropic_request:
_anthropic_request["anthropic_version"] = self.anthropic_version
anthropic_beta_list = []
user_betas = get_anthropic_beta_from_headers(headers)
if user_betas:
anthropic_beta_list.extend(user_betas)
# Auto-detect and add beta headers using the new method
tools = optional_params.get("tools")
tool_search_used = self.is_tool_search_used(tools)
programmatic_tool_calling_used = self.is_programmatic_tool_calling_used(tools)
input_examples_used = self.is_input_examples_used(tools)
beta_set = set(get_anthropic_beta_from_headers(headers))
auto_betas = self.get_anthropic_beta_list(
model=model,
custom_llm_provider=self.custom_llm_provider or "bedrock",
tools=tools,
optional_params=optional_params,
computer_tool_used=self.is_computer_tool_used(tools),
prompt_caching_set=self.is_cache_control_set(messages),
file_id_used=self.is_file_id_used(messages),
mcp_server_used=self.is_mcp_server_used(optional_params.get("mcp_servers")),
)
anthropic_beta_list.extend(auto_betas)
if anthropic_beta_list:
_anthropic_request["anthropic_beta"] = list(set(anthropic_beta_list))
beta_set.update(auto_betas)
if (
tool_search_used
and not (programmatic_tool_calling_used or input_examples_used)
):
beta_set.discard(ANTHROPIC_TOOL_SEARCH_BETA_HEADER)
if "opus-4" in model.lower() or "opus_4" in model.lower():
beta_set.add("tool-search-tool-2025-10-19")
if beta_set:
_anthropic_request["anthropic_beta"] = list(beta_set)
return _anthropic_request

38
litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
View File

@ -24,6 +24,7 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation
AmazonInvokeConfig,
)
from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers
from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER
from litellm.types.llms.openai import AllMessageValues
from litellm.types.router import GenericLiteLLMParams
from litellm.types.utils import GenericStreamingChunk
@ -142,31 +143,38 @@ class AmazonAnthropicClaudeMessagesConfig(
anthropic_messages_request.pop("model", None)
# 4. AUTO-INJECT beta headers based on features used
anthropic_beta_list = []
# Get user-provided beta headers first
user_betas = get_anthropic_beta_from_headers(headers)
if user_betas:
anthropic_beta_list.extend(user_betas)
anthropic_model_info = AnthropicModelInfo()
tools = anthropic_messages_optional_request_params.get("tools")
messages_typed = cast(List[AllMessageValues], messages)
tool_search_used = anthropic_model_info.is_tool_search_used(tools)
programmatic_tool_calling_used = anthropic_model_info.is_programmatic_tool_calling_used(
tools
)
input_examples_used = anthropic_model_info.is_input_examples_used(tools)
beta_set = set(get_anthropic_beta_from_headers(headers))
auto_betas = anthropic_model_info.get_anthropic_beta_list(
model=model,
custom_llm_provider="bedrock",
tools=tools,
optional_params=anthropic_messages_optional_request_params,
computer_tool_used=anthropic_model_info.is_computer_tool_used(tools),
prompt_caching_set=anthropic_model_info.is_cache_control_set(messages_typed),
file_id_used=anthropic_model_info.is_file_id_used(messages_typed),
mcp_server_used=anthropic_model_info.is_mcp_server_used(anthropic_messages_optional_request_params.get("mcp_servers")),
mcp_server_used=anthropic_model_info.is_mcp_server_used(
anthropic_messages_optional_request_params.get("mcp_servers")
),
)
anthropic_beta_list.extend(auto_betas)
# Remove duplicates and set in request body if any beta headers exist
if anthropic_beta_list:
anthropic_messages_request["anthropic_beta"] = list(set(anthropic_beta_list))
beta_set.update(auto_betas)
if (
tool_search_used
and not (programmatic_tool_calling_used or input_examples_used)
):
beta_set.discard(ANTHROPIC_TOOL_SEARCH_BETA_HEADER)
if "opus-4" in model.lower() or "opus_4" in model.lower():
beta_set.add("tool-search-tool-2025-10-19")
if beta_set:
anthropic_messages_request["anthropic_beta"] = list(beta_set)
return anthropic_messages_request

17
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
View File

@ -70,23 +70,22 @@ class VertexAIAnthropicConfig(AnthropicConfig):
data.pop("model", None) # vertex anthropic doesn't accept 'model' parameter
tools = optional_params.get("tools")
anthropic_beta_list = []
tool_search_used = self.is_tool_search_used(tools)
auto_betas = self.get_anthropic_beta_list(
model=model,
custom_llm_provider=self.custom_llm_provider or "vertex_ai",
tools=tools,
optional_params=optional_params,
computer_tool_used=self.is_computer_tool_used(tools),
prompt_caching_set=self.is_cache_control_set(messages),
file_id_used=self.is_file_id_used(messages),
mcp_server_used=self.is_mcp_server_used(optional_params.get("mcp_servers")),
)
anthropic_beta_list.extend(auto_betas)
# Note: VertexAI uses tool-search-tool-2025-10-19 for tool search (different from direct API)
if anthropic_beta_list:
data["anthropic_beta"] = list(set(anthropic_beta_list))
beta_set = set(auto_betas)
if tool_search_used:
beta_set.add("tool-search-tool-2025-10-19") # Vertex requires this header for tool search
if beta_set:
data["anthropic_beta"] = list(beta_set)
return data